Research ArticleDistributed Typhoon Track Prediction Based on ComplexFeatures and Multitask Learning

Yongjiao Sun1 Yaning Song 1 Baiyou Qiao1 and Boyang Li2

1School of Computer Science and Engineering Northeastern University Shenyang 110819 China2School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

Correspondence should be addressed to Yaning Song 1901780stuneueducn

Received 30 April 2021 Accepted 4 July 2021 Published 13 July 2021

Academic Editor Guanfeng Liu

Copyright copy 2021 Yongjiao Sun et al is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Typhoons are common natural phenomena that often have disastrous aftermaths particularly in coastal areas Consequentlytyphoon track prediction has always been an important research topic It chiefly involves predicting the movement of a typhoonaccording to its history However the formation and movement of typhoons is a complex process which in turn makes accurateprediction more complicated the potential location of typhoons is related to both historical and future factors Existing works donot fully consider these factors thus there is significant room for improving the accuracy of predictions To this end we presenteda novel typhoon track prediction framework comprising complex historical featuresmdashclimatic geographical and physicalfeaturesmdashas well as a deep-learning network based onmultitask learningWe implemented the framework in a distributed systemthereby improving the training efficiency of the network We verified the efficiency of the proposed framework on real datasets

1 Introduction

Typhoons are tropical cyclones that occur in the WesternPacific and adjacent waters and are common climate phe-nomena Given that typhoons have significant destructivepower and often imperil the coastal areas where they makelandfall the nature of these typhoons has long been animportant research topic [1ndash3]

Typhoon track prediction is a typical problem in ty-phoon research Traditionally typhoon paths are oftenpredicted through such methods as force analysis andmathematical statistics [4ndash7] In recent years however withthe development of artificial intelligence more researchersare using deep-learning technology to predict the movementof typhoons For example some studies have utilized cloudmaps to locate typhoons and predict their movement viaconvolutional neural networks (CNNs) and generativeadversarial networks (GANs) [89] Given that typhoon trackis a continuous process many studies also use recurrentneural networks (RNNs) and long short-term memory(LSTMs) to process the track sequence [10] e formationand movement of typhoons is a very complex process that is

affected by historical as well as future factors Although thisproblem has been widely studied some limitations remainand hinder the accurate prediction of the paths typhoonstake

Typhoons have complex historical features Existingstudies have evaluated the history of typhoons with re-spect to geopotential height wind field and atmosphericpressure however these studies did not comprehensivelyanalyse the features of previous typhoons erefore byanalysing historical data we identified additional perti-nent features and categorized them into climatic geo-graphical and physical features Further we consideredsome new featuresmdashsuch as geostrophic forcemdashfor thepurposes of this study e factors that affect typhoonmovement from many aspects were categorized undermultimodal features

Although existing works apply deep learning to evaluatetyphoons most only consider the track of a typhoon as anisolated target and ignore the multiple factors that influencethis track Likewise although a few studies have predictedtyphoon tracks via a multifaceted approach their analyses oftyphoon features are too simplistic erefore we combined

HindawiComplexityVolume 2021 Article ID 5661292 12 pageshttpsdoiorg10115520215661292

the complex features of typhoons processed the featuresthrough different learning frameworks and incorporatedmultitask learning to further improve the accuracy of ty-phoon track prediction

However the expansion of data and model parameters isaccompanied by an increase in computational power andduration of model training In this regard using distributedand parallel training methods such as SparkMLlib (httpsparkapacheorgmllib) can significantly improve the effi-ciency of model training erefore to improve the trainingefficiency of the framework proposed in this paper weimplemented it based on Ray (httpsrayio) which is anemerging distributed AI platform

e contributions of this paper are as follows

(1) We propose a typhoon track prediction frameworkthat considers both historical features and the in-teraction of multiple factors

(2) We extracted the complex featuresmdashclimatic geo-graphical and physicalmdashthat affect the movement oftyphoonsWe employed deep-learning networks anda multitask learning method to improve the accuracyof typhoon track prediction

(3) We utilized distributed implementation to improvethe training efficiency of the network

(4) We used real-life datasets to conduct the experi-ments and verify the effectiveness of the proposedframework

e remainder of this paper is organized as followsSection 2 introduces related works on typhoon track pre-diction Section 3 covers the problem definition and relatedtechnologies Section 4 introduces the proposed track pre-diction framework including feature selection and networkstructure We then verify the efficiency of the proposedframework through experiments in Section 5 and finallysummarize this paper in Section 6

2 Related Works

21 Traditional Methods Traditional methods of typhoontrack prediction include numerical statistical regressionand integrated models Weber [7] proposed a numericalmodel (STEPS) to analyse the annual performance of thenumerical orbit-prediction model the model involves a verycomplex atmospheric-dynamics formula and requiresstrong computational power to successfully predict a ty-phoonrsquos path Demaria et al [4] proposed a statistical model(SHIPS) that modifies the predictor according to the newprediction factors of every new year to make the model moresuitable for observing typhoon movement Compared withSTEPS SHIPS has a lower computational complexitynonetheless its accuracy is also relatively low Goerss andKrishnamurti et al [56] demonstrated that the integratedmodel comprising multiple models was more accurate asopposed to individual models Although traditional modelsplay a crucial role in forecasting typhoon tracks they stillhave many shortcomings With the increase in meteoro-logical detection instruments more meteorological

spatiotemporal data (big data) will be produced Howevertraditional models are inevitably becoming outmoded It isdifficult for them to capture nonlinear typhoon models fromthese huge datasets which significantly reduce the accuracyof prediction

22 Deep-Learning Methods In recent years deep learningand parameter optimization [11] have rapidly developed andprovided more powerful methods for typhoon track pre-diction Neural networks have the advantages of nonline-arity and nonlocality ey can utilize big data to train thenetwork and hence determine the mapping relationshipsbetween input and output this essentially makes the pre-dictions more accurate

CNN-based methods Wang et al [9] used 2250 in-frared satellite images to train the CNN network eaverage angular error of typhoon track prediction wasthus reduced to 278 degrees indicating the greatpotential of CNN in typhoon path prediction Giffard-Roisin et al [12] proposed a fusion neural networkcomprising a neural network using past trajectory dataand a CNN involving the reanalysis of atmosphericwind-field imagesGAN-based methods Ruttgers et al [8] used GAN inconjunction with satellite images and meteorologicaldata to forecast the central location of typhoons It hasbeen proven that GAN utilizes many features thatotherwise cannot be used by traditional models thuspreventing the otherwise inevitable errors associatedwith some traditional modelsRNN- and LSTM-based methods Moradi Kordma-halleh et al [13] used sparse RNNs with flexible to-pology in which a genetic algorithm (GA) was used tooptimize the weight connection Alemany et al [14]proposed a fully connected RNN in the grid system theproposed approach can be used to model the complexand nonlinear temporal behavior of typhoons Furtherit can accumulate the historical information of thenonlinear dynamics of the atmospheric system byupdating the weight matrix hence improving the ac-curacy of typhoon track prediction Chandra and Dayaland Chandra et al [1516] also proved that RNNs aresuitable for typhoon track prediction Lian et al [17]proposed a novel data-driven deep-learning modelcomposed of a multidimensional feature-selectionlayer a convolution layer and a gating-cycle unit layerIt uses spatial locations and a variety of meteorologicalfeatures to predict typhoon trajectories Compared withCNNs and RNNs without a feature-selection layer thenovel model has higher accuracy Using records from1949 to 2012 as the training data Gao et al [10]proposed a typhoon track prediction method based onLSTM the research shows that the model can predictthe typhoon track 6ndash24 hours in advance with betteraccuracy Kim et al [18] proposed a large number oftemporal and spatial prediction models based on theConvLSTM model

2 Complexity

Multitask learning-based methods Chandra [19]proposed a coevolutionary multitask learning algo-rithm that combines the functions of modularizationand multitask learning is approach coordinatesmultitask learning dynamic programming and co-evolution algorithms Furthermore it can trainneural networks via feature sharing and modularknowledge representation It can also be used topredict typhoon intensity with limited input [20]is shows that compared with traditional modelsthe algorithm not only solves the problem of dynamictime series but also improves the prediction accuracyMukherjee and Mitra [21] proposed a joint learningmodel that can learn the distance and direction oftyphoons simultaneously via two different structureswith multiple LSTMs and multiple fully connectedlayers initial layer parameters are shared accordingto past typhoon track data e research results showthat the model can predict direction and distance(ie displacement) simultaneously

3 Preliminaries

In this section we first introduce the relevant technologiesutilized in our framework and then proceed to define ourproblem

31CNN andResNet CNN is a type of deep-learning modelthat has been successfully implemented in image recognition[22] e convolution layer is one of the core structures ofCNNs e input of the convolution layer includes one ormore matrices of the same size each of which is called achannel Each convolution layer uses common parametersknown as convolution kernels For 2D input the function ofthe convolution layer is to weigh the corresponding sub-matrices according to the size of kernels thus the convo-lution layer output is generated

Another important structure is the pooling layer whichaims to reduce the parameters of the model and strengthenthe network while improving the computing speed estrategies of the pooling layer include the maximum andaverage pooling

ResNet is a CNN model widely used for feature ex-traction [23] To solve the migration problem in deepnetworks ResNet proposes residual learning ResNet re-places the feature H(x) obtained by convolution layers withthe residual H(x) minus x of feature and input In contrast withordinary CNN ResNet adds a shortcut mechanism betweenevery two layers to realize residual learning

32 LSTM LSTM is a special type of RNN that is delib-erately designed to avoid long-term dependence It intro-duces a gate to solve gradient disappearance or explosion[24] LSTM contains four important structures namely theforget gate the input gate the update stage and the outputgate As shown in Figure 1 this framework operates asfollows

(1) e function of the forget gate is implemented bysigmoid to determine which information needs to beforgotten according to the input xt and the outputhtminus1 of the previous cell

(2) e input gate determines the information that willbe stored in the current cell

(3) e update stage updates Ct of the current cell(4) e output gate outputs the final information to the

next cell

33 Multitask Learning In single-task learning (involved inthe previous models) the model learns only one task at atime For complex problems single-task learning decom-poses the problems into multiple independent subproblemsfor separate training and then combines them However inpractical applications these subproblems frequently containcorrelation information that is often ignored by the single-task learning method

In this regard the goal of multitask learning is to in-tegrate multiple related tasks through shared representations[25] It entails hard and soft parameter sharing Hard pa-rameter sharing shares some parameters among all tasks andonly uses the tasksrsquo unique parameters at a specific layer Insoft parameter sharing each task has unique parametersFinally the similarity is expressed by adding constraints tothe differences between parameters of different tasks

34 Problem Definition e problem of typhoon trackprediction can be expressed in terms of the features of agiven typhoon at several past instances or moments the goalis to predict the locations at certain times or instances in thefuture e past-feature sequence of the typhoon is denotedas S (s1 s2 st) where si represents the features of thetyphoon at time i and t is the length of the sequence etrack of the typhoon at the future moment isT[(xt+1 yt+1) (xt+2 yt+2) (xt+n yt+n)] where (xj yj) isthe geographical coordinate (latitude and longitude) at timetj e goal of this study is to establish the mapping modelM T⟶ S and hence calculate the future trajectory se-quence T through the historical sequence S

4 Framework

Figure 2 illustrates the structure of our proposed frameworkIt entails feature selection weighted fusion and multitaskprediction In this section we will introduce all the partsindividually

41 Features ere are three types of features in ourframework namely climatic geographical and character-istic features e climatic features include sea surfacetemperature geopotential height and specific humidityGeographical features include geostrophic forces echaracteristic features are the speed and position of thetyphoon

Complexity 3

411 Climatic Features By studying the influence of climateon typhoons we selected three main factors as climaticfeatures in this study

Sea surface temperature (SST) SST is one of the mostimportant factors in meteorological research In gen-eral SST decreases when latitude increases SST plays apivotal role in the formation and movement of ty-phoons typhoons are formed above the sea surfacewhere SST is higher than 265degC and the intensity of thetyphoon increases through continuous absorption ofenergy SST is also one of the main factors influencingthe direction of motion and landing location of ty-phoons In this study we mainly considered the regionwithin 0degN and 60degN latitudes and 100degE and 180degElongitudes e SST in this area was regularly collectedby the sensor As shown in Figure 3 we used a matrix of121 rows and 161 columns to represent SST in whichthe SST near the equator is above 30degC whereas theSST at higher latitudes is approximately 0degC We alsodistinguished land from sea the darkest shades inFigure 3 are landGeopotential height (GH) GH is an imaginary heightin meteorology expressed in terms of the work doneagainst gravity by an object of unit mass rising from sea

level to a certain height GH also plays an importantrole in maintaining the intensity and motion of ty-phoons For example the large geopotential heightgradient between the Western Pacific subtropical highand typhoon determines the direction of movement oftyphoon Ambi to a certain extent [13] We studied GHin the same region described previously In contrast to

ResNet

ResNet

ResNet

SST

GH 200 500 850 (hPa)

SH 200 500 850 (hPa)

Geographical and physical features

LSTM

LongitudeLatitude

Intensity

121 times 161 5

3 times 121 times 161

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Figure 2 e structure of our framework

σ σ Tan h σ

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Figure 1 Structure of LSTM

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4 Complexity

SST we choose three different GHs under different hPaFigures 4(a)ndash4(c) show examples of GH which is alsorepresented by matrices with 121 rows and 161 col-umns It is evident from these charts that GH increaseswith latitudeSpecific humidity (SH) SH refers to the ratio of the massof water vapor in the atmosphere to the total mass of airere is a strong relationship between typhoons andvertical air motion and SH is usually used when dis-cussing the vertical motion erefore we introduced SHas a distinct climatic feature Figures 4(d)ndash4(f) show 3 SHdata charts under different hPa We can observe that SHin the south is higher than that in the north

412 Geographical Feature (1) Geostrophic force (GF) GFalso known as the Coriolis force was derived to describe theforce exerted on moving objects on the surface of the Earthas a result of the Earthrsquos rotation Owing to the existence ofGF a rotating flow of air is formed and eventually a ty-phoon is formed under the combined action of variousfactors e typhoon is also affected by GF during itsmovement In the northern hemisphere the GF of the ty-phoon is to the right which determines the typhoonrsquos di-rection of movement to a certain extent GF can be expressedas

F 2mvω sin θ (1)

where m is the mass of the object v is the velocity of theobject ω is the angular velocity of the Earthrsquos rotation and θis the latitude of the object before it begins to move Giventhat the mass of typhoons is difficult to estimate we use thegeostrophic force gradient to represent the influence of GFon typhoons denoted as

zF

zm 2vω sin θ (2)

413 Physical Features We use physical characteristics todescribe the time series and tracks of typhoons

Location and direction Given that the track of a ty-phoon is a series of coordinates we used the latitudesand longitudes (lat lon) or offsets (ΔlatΔlon) to de-scribe the location and direction of motion of typhoonsSince typhoon data were collected every 6 hours wecalculated the movement and direction of the typhoonevery 6 hoursSpeed e typhoon data are coarse erefore we usedthe average of the velocities of the typhoon at twoconsecutive moments to describe the moving velocityof the typhoonIntensity e intensity of a typhoon is determined byits wind speed Existing studies have validated therelationship between the central pressure of a typhoonand the maximum wind speed [26] erefore we usedthe maximum central pressure to express the intensitycharacteristics of a typhoon

42 Network Owing to the different modes of features weused different networks to process the features and then usedfeature fusion for learning e entire network architectureis illustrated in Figure 2

421 Feature Extraction We used climatic geographicaland physical features Some of these were two-dimensionalmatrices whereas some were one-dimensional vectorsConsequently we used different networks for differentfeatures

For climatic features all inputs were two-dimensionalimages We therefore used three ResNets to process theimages e ResNets employed in our framework have 18hidden layers [23] as shown in Figure 5 GH and SH havetrichannel inputs whereas SST has single-channel inputefirst layer is a convolution layer e size of the convolutionkernel is 7 times 7 and the stride is (2 2) Based on the size of theinput we set padding as (3 3) Batch normalization (BN)and rectified linear units (ReLU) were also used in theconvolution layer After the convolution operation thenetwork performs a maximum-pooling operation ere arefour residual blocks after the first layer Each residual blockis repeated twice To simplify the representation the re-peated parts have been replaced by ellipses Each residualblock contains two convolution layers Each layer contains aconvolution kernel batch normalization and ReLUe sizeof the convolution kernel is 3 times 3 the stride is (1 1) and thepadding is (1 1) e output dimensions of each residualblock are 64 128 256 and 512 After the last residual blockthe network performs an average-pooling operatione lastlayer of the network is a fully connected network with 5-dimensional output

As for the geographical and physical features we used afully connected network and obtained a 5-dimensionalvector as the output For feature fusion we adopted a weightmodule e weight of each feature can be regarded as thecorrelation between the feature and track of the typhoonrough weighted feature fusion for each moment weobtained a 20-dimensional feature vector which then be-came the input of the predictor

422 Multitask Prediction Because LSTM has a consider-able advantage in the processing of sequence data we usedthe classic LSTM as the predictor e dimension of theinput was t times 20 where t is the length of the sequence asintroduced in Section 2 e training process is shown inFigure 6 First we used zero-state initialization to calibratethe weight h0 and C0 For each cell of the LSTM the input isthe i-th 20-dimensional feature vector It should be notedthat all LSTM cells share these parameters

e LSTM output is divided into two tasks e maintask involves locating the typhoon at the next moment andthe auxiliary task involves determining the central pressureof the typhoon (ie the intensity of the typhoon) We usedthe L2 norm as the loss function of the two tasks

For the main task the loss is the difference in distancebetween the real location and the predicted location of thetyphoon as follows

Complexity 5

Lmain

(x minus 1113954x)2

+(y minus 1113954y)2

1113969

(3)

where (x y) is the location of the typhoon at the nextmoment and (1113954x 1113954y) is the output of the predictor elongitude and latitude offset can also be used as input andthe corresponding loss will become the difference in theoffset For the auxiliary task the loss function is denoted as

Lauxiliary

(p minus 1113954p)2

1113969

(4)

where p is the central pressure of the typhoon and 1113954p is theprediction result

erefore the total loss of our framework is as follows

Ltotal αLmain +(1 minus α)Lauxiliary (5)

In this loss function α is a hyperparameter

43 Distributed Implementation To ensure that the pro-posed framework can handle big data and consequentlyimprove the efficiency of training we implemented a dis-tributed framework based on Ray Ray is a very populardistributed AI platform implemented via Python is fa-cilitates the rapidly distributed computing of the Python

Avg pooling

FC 5

3 times 3 conv 64

BNBN

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3 times 3 conv 512

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ReLuMax pooling

Input3 times 121 times 161

Block 1 Block 2 Block 3 Block 4

BN

7 times 7 conv 64 2padding = (3 3)

BN

ReLu

Shortcut Shortcut Shortcut Shortcut

3 times 3 conv 64 3 times 3 conv 128

hellip hellip hellip hellip

BN

ReLu

Figure 5 Details of ResNet in our framework

0deg

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(f )

Figure 4 Example of GH and SH (a) GH at 200 hPa (b) GH at 500 hPa (c) GH at 850 hPa (d) SH at 200 hPa (e) SH at 500 hPa (f ) SH at850 hPa

6 Complexity

code In the implementation each network structure (suchas convolution layer pooling layer and FC layer) isimplemented as a class also known as an actor in RayMultiple actors construct the entire network through thedata flow In the calculation process each calculation nodestarts multiple workers as the basis of calculation Each actoris assigned to the corresponding worker for execution In thetraining process the data flows through gRPC and sharedmemory to the corresponding worker for calculation Forexample in each ResNet after the calculation of the currentlayer is completed the data will flow to the worker of thenext layer ere is no data dependence between multipleResNets therefore parallel training can be realized

5 Experiments

51 Setup We use a real dataset to verify the effectiveness ofour framework e dataset is the Western Pacific Typhoontrack data from the JTWC (be TyphoonWarning Center theJoint Typhoon Warning Center) e dataset contains ty-phoon tracks from January 1 2001 to December 31 2005e attitude is from 0degN to 60degN and the longitude is from100degE to 180degE Statistics of the experimental setup areshown in Table 1

We use the metric of distance error (same as Lmain) toverify the effectiveness of our framework We first verify thebenefits of multitask learning technology to this frameworkNext we use different weights to discuss the relationshipbetween features and resultse framework is implementedby Python 3 and the experiments are conducted on a clusterin which each node has Intel Purley 4110 CPUs and TeslaP100 GPUs

52 Results In this section we will introduce the experi-mental results in the real-life dataset We report and analysethe results by changing the parameters en we choosesome real typhoon tracks to show our prediction results

Distance error with respect to multitask and single-tasklearning firstly we compare the results of multitasklearning (MTL) and single-task learning (STL) asshown in Figure 7 We can obverse that MTL can getbetter results than STL in most cases In the 6 h pre-diction results MTL is similar to STL However inother cases MTL can achieve about 20 performanceimprovement It proves that it is feasible to improve the

effect of track prediction by auxiliary tasks What ismore the best results in 6 h 24 h 48 h and 72 h areabout 40 km 70 km 220 km and 380 km which arebetter than most existing models It also proves theeffectiveness of our frameworkDistance error with respect to |T||T| secondly wereport the distance error with different size of input |T|e results are also shown in Figure 7 We find that |T|

has a great influence on our framework in differentcases e optimal value is 3 7 4 and 5 in 6 h 24 h48 h and 72 h As |T| becomes larger or smaller thedistance error gradually increases In the later experi-ments we selected the best value of |T| in each case toverify the effect of feature weight on the distance error

en we study the relationship between features andprediction results

Distance error with respect to wSSTwSST to study theeffect of SST we keep wGH and wSH unchanged andthen adjust the value of wSST from 01 to 10 e resultsare shown in Figure 8 We can obverse that SST willgreatly affect the results e best choice is to reducewSST as small as possibleDistance error with respect to wGHwGH to study therelationship between GH and prediction results wekeep wSST and wSH unchanged and then adjust thevalue of wGH from 01 to 10 As shown in Figure 9 wecan get the best results when wGH is set as 08 edifference between the best result and the worst resultin 6 h 24 h and 48 h is about 30 km to 100 km In 72 hthe difference could be more than 300 km An ap-propriate wGH can improve the results by 30 to 40e experimental results show that there is a strongcorrelation between GH and prediction resultsDistance error with respect to wSHwSH to study therelationship between SH and prediction results wekeep wSST and wGH unchanged and then adjust thevalue of wSH from 01 to 10 e results are shown inFigure 10 To get better resultswSH is smaller thanwGHIn 6 h and 24 h cases we can get the best results whenwSH is set as 01 In 48 h and 72 h cases it is better to setwSH as 03 An appropriate wSH can improve the resultby 40 to 50 e experimental results show that SHis also related to the prediction results but the cor-relation is less than GH

Zero

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Task 1

Task 2

hellip

f1 f2 ft

hellip hellip

hellip

helliphellip

Figure 6 e details of multitask prediction

Complexity 7

Table 1 Statistics of the experimental setup

Region Date range Dimension of featuresAttitude Longitude January 1 2001 to December 31 2005 SST GHSH Others0degN to 60degN 100degE to 180degE 121 times 161 3 times 121 times 161 20 times 1

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(a)

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72 h STL72 h MTL

(b)

Figure 7 Results of varying |T| (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

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Figure 8 Results of varying weight of wSST (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

8 Complexity

Case study We use some real typhoons to compare thereal tracks and the prediction results We select SaolaDamrey and Longwang that are formed in 2005 thereal tracks and 6 h prediction results are shown inFigures 11ndash13 Typhoon Saola was formed on Sep-tember 20th the average distance error of 6 h

prediction results is 4033 km Typhoon Damrey wasformed on September 21 the average distance error is4059 km the minimum error is 89 km and themaximum error is 6033 km Typhoon Longwang wasformed on September 26 the average distance error is4651 km

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Figure 9 Results of varying weight of wGH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

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r (km

)

6h prediction24h prediction

WSH

(a)

150

250

350

450

550

650

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSH

48h prediction72h prediction

(b)

Figure 10 Results of varying weight of wSH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

Complexity 9

Comparison with existing works Finally we compareour framework with several existing works [8101227]According to the previous introduction Ruttgers et al[8] introduced a GAN-based model used satelliteimages as the input and predicted locations after 6hours Gao et al [10] introduced an LSTM-basedmodel e work by Giffard-Roisin et al [12] was based

on CNN and feature fusion Lv et al [27] used the leastsquare method and FC network to predict the locationsWe still use distance error to verify the effectiveness andthe results are shown in Table 2 Compared with theseworks our framework can achieve high predictionresults especially in 48 h and 72 h cases In 72 h resultsour framework improves the accuracy by 60

20

24

28

32

36

40

136 140 144 148 152La

titud

eLongitude

Real track6 h prediction

Figure 11 6 h prediction results of Saola

16

17

18

19

20

21

112 114 116 118 120 122 124

Latit

ude

Longitude

Real track6h prediction

Figure 12 6 h prediction results of Damrey

19

20

21

22

23

24

25

26

115 120 125 130 135 140 145

Latit

ude

Longitude

Real track6h prediction

Figure 13 6 h prediction results of Longwang

10 Complexity

53 Summary In this section we verify the effect of differentparameters on the performance of our framework in the realdataset In general our framework can achieve good resultsbased on multitask and feature weighting We find that GHhas a strong correlation with the movement of typhoonsfollowed by SH and SST has the weakest correlationrough the training results the optimal prediction resultscan be obtained by selecting the appropriate parameters fordifferent scenes

6 Conclusion

In this paper we proposed a typhoon track predictionframework based on multitask learning and featureweightingWe analysed the correlation between the climaticgeographical and physical features and typhoon movementthrough the method of feature weighting We designed anetwork based on ResNet and LSTM and used a multitasklearning method to improve the prediction accuracy Weimplemented the network in a distributed platform Finallywe conducted experiments on real datasets to prove theeffectiveness of the framework In future works we willanalyse more features and use the attention mechanism toautomatically process the weight of features

Data Availability

e data are available from the corresponding author uponrequest

Conflicts of Interest

e authors declare that they have no conflicts of interest tothis work

Acknowledgments

e work was supported by the National Key RampD Programof China (Grant no 2016YFC1401902) the National NaturalScience Foundation of China (Grant no 61972077) and theLiaoNing Revitalization Talents Program (Grant noXLYC2007079)

References

[1] W Liu K Fujii Y Maruyama and F Yamazaki ldquoInundationassessment of the 2019 typhoon hagibis in Japan using multi-temporal sentinel-1 intensity imagesrdquo Remote Sensing vol 13no 4 p 639 2021

[2] J Cai Y Zhang R J Doviak Y Shrestha and P W ChanldquoDiagnosis and classification of typhoon-associated low-al-titude turbulence using HKO-TDWR radar observations and

machine learningrdquo IEEE Transactions on Geoscience andRemote Sensing vol 57 no 6 pp 3633ndash3648 2019

[3] J Li Q Zheng M Li Q Li and L Xie ldquoSpatiotemporaldistributions of ocean color elements in response to tropicalcyclone a case study of typhoon mangkhut (2018) past overthe northern south China seardquo Remote Sensing vol 13 no 4p 687 2021

[4] M Demaria MMainelli L K Shay J A Knaff and J KaplanldquoFurther improvements to the statistical hurricane intensityprediction scheme (SHIPS)rdquo Weather and Forecastingvol 20 no 4 pp 531ndash543 2005

[5] J S Goerss ldquoTropical cyclone track forecasts using an en-semble of dynamical modelsrdquo Monthly Weather Reviewvol 128 no 4 pp 1187ndash1193 2000

[6] T N Krishnamurti C M Kishtawal Z Zhang et alldquoMultimodel ensemble forecasts for weather and seasonalclimaterdquo Journal of Climate vol 13 no 23 pp 4196ndash42162000

[7] H C Weber ldquoHurricane track prediction using a statisticalensemble of numerical modelsrdquo Monthly Weather Reviewvol 131 no 5 pp 749ndash770 2003

[8] M Ruttgers S Lee S Jeon and D You ldquoPrediction of atyphoon track using a generative adversarial network andsatellite imagesrdquo Scientific Reports vol 9 no 1pp 6057ndash6115 2019

[9] C Wang Q Xu X Li et al ldquoCNN-based tropical cyclonetrack forecasting from satellite infrared imagesrdquo in Pro-ceedings of the IEEE International Geoscience and RemoteSensing Symposium pp 5811ndash5814 Waikoloa HI USASeptember 2020

[10] S Gao P Zhao B Pan et al ldquoA nowcasting model for theprediction of typhoon tracks based on a long short termmemory neural networkrdquo Acta Oceanologica Sinica vol 37no 5 pp 8ndash12 2018

[11] J Chen M Zhong J Li D Wang T Qian and H TuldquoEffective deep attributed network representation learningwith topology adapted smoothingrdquo IEEE Transactions onCybernetics 2021

[12] S Giffard-Roisin M Yang G Charpiat C Kumler BonfantiB Kegl and C Monteleoni ldquoTropical cyclone track fore-casting using fused deep learning from aligned reanalysisdatardquo Frontiers in Big Data vol 3 p 1 2020

[13] M Moradi Kordmahalleh M Gorji Sefidmazgi andA Homaifar ldquoA sparse recurrent neural network for tra-jectory prediction of atlantic hurricanesrdquo in Proceedings of theGenetic and Evolutionary Computation Conference pp 957ndash964 Lille France July 2016

[14] S Alemany J Beltran A Perez et al ldquoPredicting hurricanetrajectories using a recurrent neural networkrdquo in Proceedingsof the irty-ird AAAI Conference on Artificial Intelligencepp 468ndash475 Honolulu HI USA January 2019

[15] R Chandra and K Dayal ldquoCooperative neuro-evolution ofElman recurrent networks for tropical cyclone wind-intensityprediction in the south pacific regionrdquo in Proceedings of the

Table 2 Results compared with the existing works

6 h 24 h 48 h 72 hOur framework 3875 6954 19661 3681Gao et al [10] 4595 10568 33254 97450Giffard-Roisin et al [12] mdash 1361 mdash mdashRuttgers et al [8] 956 mdash mdash mdashLv et al [27] mdash 15834 36176 mdash

Complexity 11

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity

411 Climatic Features By studying the influence of climateon typhoons we selected three main factors as climaticfeatures in this study

Sea surface temperature (SST) SST is one of the mostimportant factors in meteorological research In gen-eral SST decreases when latitude increases SST plays apivotal role in the formation and movement of ty-phoons typhoons are formed above the sea surfacewhere SST is higher than 265degC and the intensity of thetyphoon increases through continuous absorption ofenergy SST is also one of the main factors influencingthe direction of motion and landing location of ty-phoons In this study we mainly considered the regionwithin 0degN and 60degN latitudes and 100degE and 180degElongitudes e SST in this area was regularly collectedby the sensor As shown in Figure 3 we used a matrix of121 rows and 161 columns to represent SST in whichthe SST near the equator is above 30degC whereas theSST at higher latitudes is approximately 0degC We alsodistinguished land from sea the darkest shades inFigure 3 are landGeopotential height (GH) GH is an imaginary heightin meteorology expressed in terms of the work doneagainst gravity by an object of unit mass rising from sea

level to a certain height GH also plays an importantrole in maintaining the intensity and motion of ty-phoons For example the large geopotential heightgradient between the Western Pacific subtropical highand typhoon determines the direction of movement oftyphoon Ambi to a certain extent [13] We studied GHin the same region described previously In contrast to

ResNet

ResNet

ResNet

SST

GH 200 500 850 (hPa)

SH 200 500 850 (hPa)

Geographical and physical features

LSTM

LongitudeLatitude

Intensity

121 times 161 5

3 times 121 times 161

3 times 121 times 161

5

5

5

FC

Weightedfusion

FC

FC

Figure 2 e structure of our framework

σ σ Tan h σ

Tan h

x x

xt

Ctminus1 C

t

htminus1 h

t

x +

ft

it

Ct

~ otForget gate

Input gate

Update stage

Output gate

ft = σ (W

fx

t + U

fh

tminus1 + bf)

ot = σ (W

ox

t + U

oh

tminus1 + bo)

it = σ (W

ix

t + U

ih

tminus1 + bi)

ht = o

t tan h(C

t)

= tan h (WCx

t + U

Ch

tminus1 + bC)C

t

~

Ct

~= f

tminus1Ctminus1 + it

Ct

Figure 1 Structure of LSTM

100deg

E

110deg

E

120deg

E

130deg

E

140deg

E

150deg

E

160deg

E

170deg

E

180deg

W

Sea s

urfa

ce te

mpe

ratu

re

0deg0

5

10

15

20

25

30

10degN

20degN

30degN

40degN

50degN

60degN

Figure 3 An example of SST

4 Complexity

SST we choose three different GHs under different hPaFigures 4(a)ndash4(c) show examples of GH which is alsorepresented by matrices with 121 rows and 161 col-umns It is evident from these charts that GH increaseswith latitudeSpecific humidity (SH) SH refers to the ratio of the massof water vapor in the atmosphere to the total mass of airere is a strong relationship between typhoons andvertical air motion and SH is usually used when dis-cussing the vertical motion erefore we introduced SHas a distinct climatic feature Figures 4(d)ndash4(f) show 3 SHdata charts under different hPa We can observe that SHin the south is higher than that in the north

412 Geographical Feature (1) Geostrophic force (GF) GFalso known as the Coriolis force was derived to describe theforce exerted on moving objects on the surface of the Earthas a result of the Earthrsquos rotation Owing to the existence ofGF a rotating flow of air is formed and eventually a ty-phoon is formed under the combined action of variousfactors e typhoon is also affected by GF during itsmovement In the northern hemisphere the GF of the ty-phoon is to the right which determines the typhoonrsquos di-rection of movement to a certain extent GF can be expressedas

F 2mvω sin θ (1)

where m is the mass of the object v is the velocity of theobject ω is the angular velocity of the Earthrsquos rotation and θis the latitude of the object before it begins to move Giventhat the mass of typhoons is difficult to estimate we use thegeostrophic force gradient to represent the influence of GFon typhoons denoted as

zF

zm 2vω sin θ (2)

413 Physical Features We use physical characteristics todescribe the time series and tracks of typhoons

Location and direction Given that the track of a ty-phoon is a series of coordinates we used the latitudesand longitudes (lat lon) or offsets (ΔlatΔlon) to de-scribe the location and direction of motion of typhoonsSince typhoon data were collected every 6 hours wecalculated the movement and direction of the typhoonevery 6 hoursSpeed e typhoon data are coarse erefore we usedthe average of the velocities of the typhoon at twoconsecutive moments to describe the moving velocityof the typhoonIntensity e intensity of a typhoon is determined byits wind speed Existing studies have validated therelationship between the central pressure of a typhoonand the maximum wind speed [26] erefore we usedthe maximum central pressure to express the intensitycharacteristics of a typhoon

42 Network Owing to the different modes of features weused different networks to process the features and then usedfeature fusion for learning e entire network architectureis illustrated in Figure 2

421 Feature Extraction We used climatic geographicaland physical features Some of these were two-dimensionalmatrices whereas some were one-dimensional vectorsConsequently we used different networks for differentfeatures

For climatic features all inputs were two-dimensionalimages We therefore used three ResNets to process theimages e ResNets employed in our framework have 18hidden layers [23] as shown in Figure 5 GH and SH havetrichannel inputs whereas SST has single-channel inputefirst layer is a convolution layer e size of the convolutionkernel is 7 times 7 and the stride is (2 2) Based on the size of theinput we set padding as (3 3) Batch normalization (BN)and rectified linear units (ReLU) were also used in theconvolution layer After the convolution operation thenetwork performs a maximum-pooling operation ere arefour residual blocks after the first layer Each residual blockis repeated twice To simplify the representation the re-peated parts have been replaced by ellipses Each residualblock contains two convolution layers Each layer contains aconvolution kernel batch normalization and ReLUe sizeof the convolution kernel is 3 times 3 the stride is (1 1) and thepadding is (1 1) e output dimensions of each residualblock are 64 128 256 and 512 After the last residual blockthe network performs an average-pooling operatione lastlayer of the network is a fully connected network with 5-dimensional output

As for the geographical and physical features we used afully connected network and obtained a 5-dimensionalvector as the output For feature fusion we adopted a weightmodule e weight of each feature can be regarded as thecorrelation between the feature and track of the typhoonrough weighted feature fusion for each moment weobtained a 20-dimensional feature vector which then be-came the input of the predictor

422 Multitask Prediction Because LSTM has a consider-able advantage in the processing of sequence data we usedthe classic LSTM as the predictor e dimension of theinput was t times 20 where t is the length of the sequence asintroduced in Section 2 e training process is shown inFigure 6 First we used zero-state initialization to calibratethe weight h0 and C0 For each cell of the LSTM the input isthe i-th 20-dimensional feature vector It should be notedthat all LSTM cells share these parameters

e LSTM output is divided into two tasks e maintask involves locating the typhoon at the next moment andthe auxiliary task involves determining the central pressureof the typhoon (ie the intensity of the typhoon) We usedthe L2 norm as the loss function of the two tasks

For the main task the loss is the difference in distancebetween the real location and the predicted location of thetyphoon as follows

Complexity 5

Lmain

(x minus 1113954x)2

+(y minus 1113954y)2

1113969

(3)

where (x y) is the location of the typhoon at the nextmoment and (1113954x 1113954y) is the output of the predictor elongitude and latitude offset can also be used as input andthe corresponding loss will become the difference in theoffset For the auxiliary task the loss function is denoted as

Lauxiliary

(p minus 1113954p)2

1113969

(4)

where p is the central pressure of the typhoon and 1113954p is theprediction result

erefore the total loss of our framework is as follows

Ltotal αLmain +(1 minus α)Lauxiliary (5)

In this loss function α is a hyperparameter

43 Distributed Implementation To ensure that the pro-posed framework can handle big data and consequentlyimprove the efficiency of training we implemented a dis-tributed framework based on Ray Ray is a very populardistributed AI platform implemented via Python is fa-cilitates the rapidly distributed computing of the Python

Avg pooling

FC 5

3 times 3 conv 64

BNBN

ReLu

3 times 3 conv 128

BN

ReLu

BNBN

ReLu

3 times 3 conv 256

BN

ReLu

3 times 3 conv 256

BN

ReLu

3x3 conv 512

ReLu

3 times 3 conv 512

BN

ReLu

3 times 3 conv 512

BN

ReLuMax pooling

Input3 times 121 times 161

Block 1 Block 2 Block 3 Block 4

BN

7 times 7 conv 64 2padding = (3 3)

BN

ReLu

Shortcut Shortcut Shortcut Shortcut

3 times 3 conv 64 3 times 3 conv 128

hellip hellip hellip hellip

BN

ReLu

Figure 5 Details of ResNet in our framework

0deg

10degN

20degN

30degN

40degN

50degN

60degN

11600

11800

12000

12200

12400

Geo

pote

ntia

l hei

ght (

200

hPa)

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(a)

0deg

10degN

20degN

30degN

40degN

50degN

60degN

5400

5500

5600

5700

5800

Geo

pote

ntia

l hei

ght (

500

hPa)

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(b)

0deg1350

Geo

pote

ntia

l hei

ght (

850

hPa)

1400

1450

1500

1550

10degN

20degN

30degN

40degN

50degN

60degN

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(c)

0deg

10degN

20degN

30degN

40degN

50degN

60degNSp

ecifi

c hum

idity

(200

hPa)

00000

00002

00004

00006

00008

00010

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(d)

0deg 00000 Spec

ific h

umid

ity (5

00 h

Pa)

00005000100001500020000250003000035

10degN

20degN

30degN

40degN

50degN

60degN

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W(e)

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

0000 Spec

ific h

umid

ity (8

50hP

a)

000200040006000800100012

0deg

10degN

20degN

30degN

40degN

50degN

60degN

(f )

Figure 4 Example of GH and SH (a) GH at 200 hPa (b) GH at 500 hPa (c) GH at 850 hPa (d) SH at 200 hPa (e) SH at 500 hPa (f ) SH at850 hPa

6 Complexity

code In the implementation each network structure (suchas convolution layer pooling layer and FC layer) isimplemented as a class also known as an actor in RayMultiple actors construct the entire network through thedata flow In the calculation process each calculation nodestarts multiple workers as the basis of calculation Each actoris assigned to the corresponding worker for execution In thetraining process the data flows through gRPC and sharedmemory to the corresponding worker for calculation Forexample in each ResNet after the calculation of the currentlayer is completed the data will flow to the worker of thenext layer ere is no data dependence between multipleResNets therefore parallel training can be realized

5 Experiments

51 Setup We use a real dataset to verify the effectiveness ofour framework e dataset is the Western Pacific Typhoontrack data from the JTWC (be TyphoonWarning Center theJoint Typhoon Warning Center) e dataset contains ty-phoon tracks from January 1 2001 to December 31 2005e attitude is from 0degN to 60degN and the longitude is from100degE to 180degE Statistics of the experimental setup areshown in Table 1

We use the metric of distance error (same as Lmain) toverify the effectiveness of our framework We first verify thebenefits of multitask learning technology to this frameworkNext we use different weights to discuss the relationshipbetween features and resultse framework is implementedby Python 3 and the experiments are conducted on a clusterin which each node has Intel Purley 4110 CPUs and TeslaP100 GPUs

52 Results In this section we will introduce the experi-mental results in the real-life dataset We report and analysethe results by changing the parameters en we choosesome real typhoon tracks to show our prediction results

Distance error with respect to multitask and single-tasklearning firstly we compare the results of multitasklearning (MTL) and single-task learning (STL) asshown in Figure 7 We can obverse that MTL can getbetter results than STL in most cases In the 6 h pre-diction results MTL is similar to STL However inother cases MTL can achieve about 20 performanceimprovement It proves that it is feasible to improve the

effect of track prediction by auxiliary tasks What ismore the best results in 6 h 24 h 48 h and 72 h areabout 40 km 70 km 220 km and 380 km which arebetter than most existing models It also proves theeffectiveness of our frameworkDistance error with respect to |T||T| secondly wereport the distance error with different size of input |T|e results are also shown in Figure 7 We find that |T|

has a great influence on our framework in differentcases e optimal value is 3 7 4 and 5 in 6 h 24 h48 h and 72 h As |T| becomes larger or smaller thedistance error gradually increases In the later experi-ments we selected the best value of |T| in each case toverify the effect of feature weight on the distance error

en we study the relationship between features andprediction results

Distance error with respect to wSSTwSST to study theeffect of SST we keep wGH and wSH unchanged andthen adjust the value of wSST from 01 to 10 e resultsare shown in Figure 8 We can obverse that SST willgreatly affect the results e best choice is to reducewSST as small as possibleDistance error with respect to wGHwGH to study therelationship between GH and prediction results wekeep wSST and wSH unchanged and then adjust thevalue of wGH from 01 to 10 As shown in Figure 9 wecan get the best results when wGH is set as 08 edifference between the best result and the worst resultin 6 h 24 h and 48 h is about 30 km to 100 km In 72 hthe difference could be more than 300 km An ap-propriate wGH can improve the results by 30 to 40e experimental results show that there is a strongcorrelation between GH and prediction resultsDistance error with respect to wSHwSH to study therelationship between SH and prediction results wekeep wSST and wGH unchanged and then adjust thevalue of wSH from 01 to 10 e results are shown inFigure 10 To get better resultswSH is smaller thanwGHIn 6 h and 24 h cases we can get the best results whenwSH is set as 01 In 48 h and 72 h cases it is better to setwSH as 03 An appropriate wSH can improve the resultby 40 to 50 e experimental results show that SHis also related to the prediction results but the cor-relation is less than GH

Zero

-sta

te in

itial

izat

ion

LSTM cell LSTM cell LSTM cellhellip

Task 1

Task 2

hellip

f1 f2 ft

hellip hellip

hellip

helliphellip

Figure 6 e details of multitask prediction

Complexity 7

Table 1 Statistics of the experimental setup

Region Date range Dimension of featuresAttitude Longitude January 1 2001 to December 31 2005 SST GHSH Others0degN to 60degN 100degE to 180degE 121 times 161 3 times 121 times 161 20 times 1

40

80

120

160

200

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

6 h STL6 h MTL

24 h STL24 h MTL

(a)

200

300

400

500

600

700

800

900

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

48 h STL48 h MTL

72 h STL72 h MTL

(b)

Figure 7 Results of varying |T| (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

250

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSST

(a)

0

200

400

600

800

1000

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSST

48h prediction72h prediction

(b)

Figure 8 Results of varying weight of wSST (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

8 Complexity

Case study We use some real typhoons to compare thereal tracks and the prediction results We select SaolaDamrey and Longwang that are formed in 2005 thereal tracks and 6 h prediction results are shown inFigures 11ndash13 Typhoon Saola was formed on Sep-tember 20th the average distance error of 6 h

prediction results is 4033 km Typhoon Damrey wasformed on September 21 the average distance error is4059 km the minimum error is 89 km and themaximum error is 6033 km Typhoon Longwang wasformed on September 26 the average distance error is4651 km

20

40

60

80

100

120

140

160

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WGH

(a)

100

200

300

400

500

600

700

800

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WGH

48h prediction72h prediction

(b)

Figure 9 Results of varying weight of wGH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSH

(a)

150

250

350

450

550

650

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSH

48h prediction72h prediction

(b)

Figure 10 Results of varying weight of wSH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

Complexity 9

Comparison with existing works Finally we compareour framework with several existing works [8101227]According to the previous introduction Ruttgers et al[8] introduced a GAN-based model used satelliteimages as the input and predicted locations after 6hours Gao et al [10] introduced an LSTM-basedmodel e work by Giffard-Roisin et al [12] was based

on CNN and feature fusion Lv et al [27] used the leastsquare method and FC network to predict the locationsWe still use distance error to verify the effectiveness andthe results are shown in Table 2 Compared with theseworks our framework can achieve high predictionresults especially in 48 h and 72 h cases In 72 h resultsour framework improves the accuracy by 60

20

24

28

32

36

40

136 140 144 148 152La

titud

eLongitude

Real track6 h prediction

Figure 11 6 h prediction results of Saola

16

17

18

19

20

21

112 114 116 118 120 122 124

Latit

ude

Longitude

Real track6h prediction

Figure 12 6 h prediction results of Damrey

19

20

21

22

23

24

25

26

115 120 125 130 135 140 145

Latit

ude

Longitude

Real track6h prediction

Figure 13 6 h prediction results of Longwang

10 Complexity

53 Summary In this section we verify the effect of differentparameters on the performance of our framework in the realdataset In general our framework can achieve good resultsbased on multitask and feature weighting We find that GHhas a strong correlation with the movement of typhoonsfollowed by SH and SST has the weakest correlationrough the training results the optimal prediction resultscan be obtained by selecting the appropriate parameters fordifferent scenes

6 Conclusion

In this paper we proposed a typhoon track predictionframework based on multitask learning and featureweightingWe analysed the correlation between the climaticgeographical and physical features and typhoon movementthrough the method of feature weighting We designed anetwork based on ResNet and LSTM and used a multitasklearning method to improve the prediction accuracy Weimplemented the network in a distributed platform Finallywe conducted experiments on real datasets to prove theeffectiveness of the framework In future works we willanalyse more features and use the attention mechanism toautomatically process the weight of features

Data Availability

e data are available from the corresponding author uponrequest

Conflicts of Interest

e authors declare that they have no conflicts of interest tothis work

Acknowledgments

e work was supported by the National Key RampD Programof China (Grant no 2016YFC1401902) the National NaturalScience Foundation of China (Grant no 61972077) and theLiaoNing Revitalization Talents Program (Grant noXLYC2007079)

References

[1] W Liu K Fujii Y Maruyama and F Yamazaki ldquoInundationassessment of the 2019 typhoon hagibis in Japan using multi-temporal sentinel-1 intensity imagesrdquo Remote Sensing vol 13no 4 p 639 2021

[2] J Cai Y Zhang R J Doviak Y Shrestha and P W ChanldquoDiagnosis and classification of typhoon-associated low-al-titude turbulence using HKO-TDWR radar observations and

machine learningrdquo IEEE Transactions on Geoscience andRemote Sensing vol 57 no 6 pp 3633ndash3648 2019

[3] J Li Q Zheng M Li Q Li and L Xie ldquoSpatiotemporaldistributions of ocean color elements in response to tropicalcyclone a case study of typhoon mangkhut (2018) past overthe northern south China seardquo Remote Sensing vol 13 no 4p 687 2021

[4] M Demaria MMainelli L K Shay J A Knaff and J KaplanldquoFurther improvements to the statistical hurricane intensityprediction scheme (SHIPS)rdquo Weather and Forecastingvol 20 no 4 pp 531ndash543 2005

[5] J S Goerss ldquoTropical cyclone track forecasts using an en-semble of dynamical modelsrdquo Monthly Weather Reviewvol 128 no 4 pp 1187ndash1193 2000

[6] T N Krishnamurti C M Kishtawal Z Zhang et alldquoMultimodel ensemble forecasts for weather and seasonalclimaterdquo Journal of Climate vol 13 no 23 pp 4196ndash42162000

[7] H C Weber ldquoHurricane track prediction using a statisticalensemble of numerical modelsrdquo Monthly Weather Reviewvol 131 no 5 pp 749ndash770 2003

[8] M Ruttgers S Lee S Jeon and D You ldquoPrediction of atyphoon track using a generative adversarial network andsatellite imagesrdquo Scientific Reports vol 9 no 1pp 6057ndash6115 2019

[9] C Wang Q Xu X Li et al ldquoCNN-based tropical cyclonetrack forecasting from satellite infrared imagesrdquo in Pro-ceedings of the IEEE International Geoscience and RemoteSensing Symposium pp 5811ndash5814 Waikoloa HI USASeptember 2020

[10] S Gao P Zhao B Pan et al ldquoA nowcasting model for theprediction of typhoon tracks based on a long short termmemory neural networkrdquo Acta Oceanologica Sinica vol 37no 5 pp 8ndash12 2018

[11] J Chen M Zhong J Li D Wang T Qian and H TuldquoEffective deep attributed network representation learningwith topology adapted smoothingrdquo IEEE Transactions onCybernetics 2021

[12] S Giffard-Roisin M Yang G Charpiat C Kumler BonfantiB Kegl and C Monteleoni ldquoTropical cyclone track fore-casting using fused deep learning from aligned reanalysisdatardquo Frontiers in Big Data vol 3 p 1 2020

[13] M Moradi Kordmahalleh M Gorji Sefidmazgi andA Homaifar ldquoA sparse recurrent neural network for tra-jectory prediction of atlantic hurricanesrdquo in Proceedings of theGenetic and Evolutionary Computation Conference pp 957ndash964 Lille France July 2016

[14] S Alemany J Beltran A Perez et al ldquoPredicting hurricanetrajectories using a recurrent neural networkrdquo in Proceedingsof the irty-ird AAAI Conference on Artificial Intelligencepp 468ndash475 Honolulu HI USA January 2019

[15] R Chandra and K Dayal ldquoCooperative neuro-evolution ofElman recurrent networks for tropical cyclone wind-intensityprediction in the south pacific regionrdquo in Proceedings of the

Table 2 Results compared with the existing works

6 h 24 h 48 h 72 hOur framework 3875 6954 19661 3681Gao et al [10] 4595 10568 33254 97450Giffard-Roisin et al [12] mdash 1361 mdash mdashRuttgers et al [8] 956 mdash mdash mdashLv et al [27] mdash 15834 36176 mdash

Complexity 11

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity

SST we choose three different GHs under different hPaFigures 4(a)ndash4(c) show examples of GH which is alsorepresented by matrices with 121 rows and 161 col-umns It is evident from these charts that GH increaseswith latitudeSpecific humidity (SH) SH refers to the ratio of the massof water vapor in the atmosphere to the total mass of airere is a strong relationship between typhoons andvertical air motion and SH is usually used when dis-cussing the vertical motion erefore we introduced SHas a distinct climatic feature Figures 4(d)ndash4(f) show 3 SHdata charts under different hPa We can observe that SHin the south is higher than that in the north

412 Geographical Feature (1) Geostrophic force (GF) GFalso known as the Coriolis force was derived to describe theforce exerted on moving objects on the surface of the Earthas a result of the Earthrsquos rotation Owing to the existence ofGF a rotating flow of air is formed and eventually a ty-phoon is formed under the combined action of variousfactors e typhoon is also affected by GF during itsmovement In the northern hemisphere the GF of the ty-phoon is to the right which determines the typhoonrsquos di-rection of movement to a certain extent GF can be expressedas

F 2mvω sin θ (1)

where m is the mass of the object v is the velocity of theobject ω is the angular velocity of the Earthrsquos rotation and θis the latitude of the object before it begins to move Giventhat the mass of typhoons is difficult to estimate we use thegeostrophic force gradient to represent the influence of GFon typhoons denoted as

zF

zm 2vω sin θ (2)

413 Physical Features We use physical characteristics todescribe the time series and tracks of typhoons

Location and direction Given that the track of a ty-phoon is a series of coordinates we used the latitudesand longitudes (lat lon) or offsets (ΔlatΔlon) to de-scribe the location and direction of motion of typhoonsSince typhoon data were collected every 6 hours wecalculated the movement and direction of the typhoonevery 6 hoursSpeed e typhoon data are coarse erefore we usedthe average of the velocities of the typhoon at twoconsecutive moments to describe the moving velocityof the typhoonIntensity e intensity of a typhoon is determined byits wind speed Existing studies have validated therelationship between the central pressure of a typhoonand the maximum wind speed [26] erefore we usedthe maximum central pressure to express the intensitycharacteristics of a typhoon

42 Network Owing to the different modes of features weused different networks to process the features and then usedfeature fusion for learning e entire network architectureis illustrated in Figure 2

421 Feature Extraction We used climatic geographicaland physical features Some of these were two-dimensionalmatrices whereas some were one-dimensional vectorsConsequently we used different networks for differentfeatures

For climatic features all inputs were two-dimensionalimages We therefore used three ResNets to process theimages e ResNets employed in our framework have 18hidden layers [23] as shown in Figure 5 GH and SH havetrichannel inputs whereas SST has single-channel inputefirst layer is a convolution layer e size of the convolutionkernel is 7 times 7 and the stride is (2 2) Based on the size of theinput we set padding as (3 3) Batch normalization (BN)and rectified linear units (ReLU) were also used in theconvolution layer After the convolution operation thenetwork performs a maximum-pooling operation ere arefour residual blocks after the first layer Each residual blockis repeated twice To simplify the representation the re-peated parts have been replaced by ellipses Each residualblock contains two convolution layers Each layer contains aconvolution kernel batch normalization and ReLUe sizeof the convolution kernel is 3 times 3 the stride is (1 1) and thepadding is (1 1) e output dimensions of each residualblock are 64 128 256 and 512 After the last residual blockthe network performs an average-pooling operatione lastlayer of the network is a fully connected network with 5-dimensional output

As for the geographical and physical features we used afully connected network and obtained a 5-dimensionalvector as the output For feature fusion we adopted a weightmodule e weight of each feature can be regarded as thecorrelation between the feature and track of the typhoonrough weighted feature fusion for each moment weobtained a 20-dimensional feature vector which then be-came the input of the predictor

422 Multitask Prediction Because LSTM has a consider-able advantage in the processing of sequence data we usedthe classic LSTM as the predictor e dimension of theinput was t times 20 where t is the length of the sequence asintroduced in Section 2 e training process is shown inFigure 6 First we used zero-state initialization to calibratethe weight h0 and C0 For each cell of the LSTM the input isthe i-th 20-dimensional feature vector It should be notedthat all LSTM cells share these parameters

e LSTM output is divided into two tasks e maintask involves locating the typhoon at the next moment andthe auxiliary task involves determining the central pressureof the typhoon (ie the intensity of the typhoon) We usedthe L2 norm as the loss function of the two tasks

For the main task the loss is the difference in distancebetween the real location and the predicted location of thetyphoon as follows

Complexity 5

Lmain

(x minus 1113954x)2

+(y minus 1113954y)2

1113969

(3)

where (x y) is the location of the typhoon at the nextmoment and (1113954x 1113954y) is the output of the predictor elongitude and latitude offset can also be used as input andthe corresponding loss will become the difference in theoffset For the auxiliary task the loss function is denoted as

Lauxiliary

(p minus 1113954p)2

1113969

(4)

where p is the central pressure of the typhoon and 1113954p is theprediction result

erefore the total loss of our framework is as follows

Ltotal αLmain +(1 minus α)Lauxiliary (5)

In this loss function α is a hyperparameter

43 Distributed Implementation To ensure that the pro-posed framework can handle big data and consequentlyimprove the efficiency of training we implemented a dis-tributed framework based on Ray Ray is a very populardistributed AI platform implemented via Python is fa-cilitates the rapidly distributed computing of the Python

Avg pooling

FC 5

3 times 3 conv 64

BNBN

ReLu

3 times 3 conv 128

BN

ReLu

BNBN

ReLu

3 times 3 conv 256

BN

ReLu

3 times 3 conv 256

BN

ReLu

3x3 conv 512

ReLu

3 times 3 conv 512

BN

ReLu

3 times 3 conv 512

BN

ReLuMax pooling

Input3 times 121 times 161

Block 1 Block 2 Block 3 Block 4

BN

7 times 7 conv 64 2padding = (3 3)

BN

ReLu

Shortcut Shortcut Shortcut Shortcut

3 times 3 conv 64 3 times 3 conv 128

hellip hellip hellip hellip

BN

ReLu

Figure 5 Details of ResNet in our framework

0deg

10degN

20degN

30degN

40degN

50degN

60degN

11600

11800

12000

12200

12400

Geo

pote

ntia

l hei

ght (

200

hPa)

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(a)

0deg

10degN

20degN

30degN

40degN

50degN

60degN

5400

5500

5600

5700

5800

Geo

pote

ntia

l hei

ght (

500

hPa)

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(b)

0deg1350

Geo

pote

ntia

l hei

ght (

850

hPa)

1400

1450

1500

1550

10degN

20degN

30degN

40degN

50degN

60degN

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(c)

0deg

10degN

20degN

30degN

40degN

50degN

60degNSp

ecifi

c hum

idity

(200

hPa)

00000

00002

00004

00006

00008

00010

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(d)

0deg 00000 Spec

ific h

umid

ity (5

00 h

Pa)

00005000100001500020000250003000035

10degN

20degN

30degN

40degN

50degN

60degN

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W(e)

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

0000 Spec

ific h

umid

ity (8

50hP

a)

000200040006000800100012

0deg

10degN

20degN

30degN

40degN

50degN

60degN

(f )

Figure 4 Example of GH and SH (a) GH at 200 hPa (b) GH at 500 hPa (c) GH at 850 hPa (d) SH at 200 hPa (e) SH at 500 hPa (f ) SH at850 hPa

6 Complexity

code In the implementation each network structure (suchas convolution layer pooling layer and FC layer) isimplemented as a class also known as an actor in RayMultiple actors construct the entire network through thedata flow In the calculation process each calculation nodestarts multiple workers as the basis of calculation Each actoris assigned to the corresponding worker for execution In thetraining process the data flows through gRPC and sharedmemory to the corresponding worker for calculation Forexample in each ResNet after the calculation of the currentlayer is completed the data will flow to the worker of thenext layer ere is no data dependence between multipleResNets therefore parallel training can be realized

5 Experiments

51 Setup We use a real dataset to verify the effectiveness ofour framework e dataset is the Western Pacific Typhoontrack data from the JTWC (be TyphoonWarning Center theJoint Typhoon Warning Center) e dataset contains ty-phoon tracks from January 1 2001 to December 31 2005e attitude is from 0degN to 60degN and the longitude is from100degE to 180degE Statistics of the experimental setup areshown in Table 1

We use the metric of distance error (same as Lmain) toverify the effectiveness of our framework We first verify thebenefits of multitask learning technology to this frameworkNext we use different weights to discuss the relationshipbetween features and resultse framework is implementedby Python 3 and the experiments are conducted on a clusterin which each node has Intel Purley 4110 CPUs and TeslaP100 GPUs

52 Results In this section we will introduce the experi-mental results in the real-life dataset We report and analysethe results by changing the parameters en we choosesome real typhoon tracks to show our prediction results

Distance error with respect to multitask and single-tasklearning firstly we compare the results of multitasklearning (MTL) and single-task learning (STL) asshown in Figure 7 We can obverse that MTL can getbetter results than STL in most cases In the 6 h pre-diction results MTL is similar to STL However inother cases MTL can achieve about 20 performanceimprovement It proves that it is feasible to improve the

effect of track prediction by auxiliary tasks What ismore the best results in 6 h 24 h 48 h and 72 h areabout 40 km 70 km 220 km and 380 km which arebetter than most existing models It also proves theeffectiveness of our frameworkDistance error with respect to |T||T| secondly wereport the distance error with different size of input |T|e results are also shown in Figure 7 We find that |T|

has a great influence on our framework in differentcases e optimal value is 3 7 4 and 5 in 6 h 24 h48 h and 72 h As |T| becomes larger or smaller thedistance error gradually increases In the later experi-ments we selected the best value of |T| in each case toverify the effect of feature weight on the distance error

en we study the relationship between features andprediction results

Distance error with respect to wSSTwSST to study theeffect of SST we keep wGH and wSH unchanged andthen adjust the value of wSST from 01 to 10 e resultsare shown in Figure 8 We can obverse that SST willgreatly affect the results e best choice is to reducewSST as small as possibleDistance error with respect to wGHwGH to study therelationship between GH and prediction results wekeep wSST and wSH unchanged and then adjust thevalue of wGH from 01 to 10 As shown in Figure 9 wecan get the best results when wGH is set as 08 edifference between the best result and the worst resultin 6 h 24 h and 48 h is about 30 km to 100 km In 72 hthe difference could be more than 300 km An ap-propriate wGH can improve the results by 30 to 40e experimental results show that there is a strongcorrelation between GH and prediction resultsDistance error with respect to wSHwSH to study therelationship between SH and prediction results wekeep wSST and wGH unchanged and then adjust thevalue of wSH from 01 to 10 e results are shown inFigure 10 To get better resultswSH is smaller thanwGHIn 6 h and 24 h cases we can get the best results whenwSH is set as 01 In 48 h and 72 h cases it is better to setwSH as 03 An appropriate wSH can improve the resultby 40 to 50 e experimental results show that SHis also related to the prediction results but the cor-relation is less than GH

Zero

-sta

te in

itial

izat

ion

LSTM cell LSTM cell LSTM cellhellip

Task 1

Task 2

hellip

f1 f2 ft

hellip hellip

hellip

helliphellip

Figure 6 e details of multitask prediction

Complexity 7

Table 1 Statistics of the experimental setup

Region Date range Dimension of featuresAttitude Longitude January 1 2001 to December 31 2005 SST GHSH Others0degN to 60degN 100degE to 180degE 121 times 161 3 times 121 times 161 20 times 1

40

80

120

160

200

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

6 h STL6 h MTL

24 h STL24 h MTL

(a)

200

300

400

500

600

700

800

900

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

48 h STL48 h MTL

72 h STL72 h MTL

(b)

Figure 7 Results of varying |T| (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

250

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSST

(a)

0

200

400

600

800

1000

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSST

48h prediction72h prediction

(b)

Figure 8 Results of varying weight of wSST (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

8 Complexity

Case study We use some real typhoons to compare thereal tracks and the prediction results We select SaolaDamrey and Longwang that are formed in 2005 thereal tracks and 6 h prediction results are shown inFigures 11ndash13 Typhoon Saola was formed on Sep-tember 20th the average distance error of 6 h

prediction results is 4033 km Typhoon Damrey wasformed on September 21 the average distance error is4059 km the minimum error is 89 km and themaximum error is 6033 km Typhoon Longwang wasformed on September 26 the average distance error is4651 km

20

40

60

80

100

120

140

160

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WGH

(a)

100

200

300

400

500

600

700

800

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WGH

48h prediction72h prediction

(b)

Figure 9 Results of varying weight of wGH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSH

(a)

150

250

350

450

550

650

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSH

48h prediction72h prediction

(b)

Figure 10 Results of varying weight of wSH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

Complexity 9

Comparison with existing works Finally we compareour framework with several existing works [8101227]According to the previous introduction Ruttgers et al[8] introduced a GAN-based model used satelliteimages as the input and predicted locations after 6hours Gao et al [10] introduced an LSTM-basedmodel e work by Giffard-Roisin et al [12] was based

on CNN and feature fusion Lv et al [27] used the leastsquare method and FC network to predict the locationsWe still use distance error to verify the effectiveness andthe results are shown in Table 2 Compared with theseworks our framework can achieve high predictionresults especially in 48 h and 72 h cases In 72 h resultsour framework improves the accuracy by 60

20

24

28

32

36

40

136 140 144 148 152La

titud

eLongitude

Real track6 h prediction

Figure 11 6 h prediction results of Saola

16

17

18

19

20

21

112 114 116 118 120 122 124

Latit

ude

Longitude

Real track6h prediction

Figure 12 6 h prediction results of Damrey

19

20

21

22

23

24

25

26

115 120 125 130 135 140 145

Latit

ude

Longitude

Real track6h prediction

Figure 13 6 h prediction results of Longwang

10 Complexity

53 Summary In this section we verify the effect of differentparameters on the performance of our framework in the realdataset In general our framework can achieve good resultsbased on multitask and feature weighting We find that GHhas a strong correlation with the movement of typhoonsfollowed by SH and SST has the weakest correlationrough the training results the optimal prediction resultscan be obtained by selecting the appropriate parameters fordifferent scenes

6 Conclusion

In this paper we proposed a typhoon track predictionframework based on multitask learning and featureweightingWe analysed the correlation between the climaticgeographical and physical features and typhoon movementthrough the method of feature weighting We designed anetwork based on ResNet and LSTM and used a multitasklearning method to improve the prediction accuracy Weimplemented the network in a distributed platform Finallywe conducted experiments on real datasets to prove theeffectiveness of the framework In future works we willanalyse more features and use the attention mechanism toautomatically process the weight of features

Data Availability

e data are available from the corresponding author uponrequest

Conflicts of Interest

e authors declare that they have no conflicts of interest tothis work

Acknowledgments

e work was supported by the National Key RampD Programof China (Grant no 2016YFC1401902) the National NaturalScience Foundation of China (Grant no 61972077) and theLiaoNing Revitalization Talents Program (Grant noXLYC2007079)

References

[1] W Liu K Fujii Y Maruyama and F Yamazaki ldquoInundationassessment of the 2019 typhoon hagibis in Japan using multi-temporal sentinel-1 intensity imagesrdquo Remote Sensing vol 13no 4 p 639 2021

[2] J Cai Y Zhang R J Doviak Y Shrestha and P W ChanldquoDiagnosis and classification of typhoon-associated low-al-titude turbulence using HKO-TDWR radar observations and

machine learningrdquo IEEE Transactions on Geoscience andRemote Sensing vol 57 no 6 pp 3633ndash3648 2019

[3] J Li Q Zheng M Li Q Li and L Xie ldquoSpatiotemporaldistributions of ocean color elements in response to tropicalcyclone a case study of typhoon mangkhut (2018) past overthe northern south China seardquo Remote Sensing vol 13 no 4p 687 2021

[4] M Demaria MMainelli L K Shay J A Knaff and J KaplanldquoFurther improvements to the statistical hurricane intensityprediction scheme (SHIPS)rdquo Weather and Forecastingvol 20 no 4 pp 531ndash543 2005

[5] J S Goerss ldquoTropical cyclone track forecasts using an en-semble of dynamical modelsrdquo Monthly Weather Reviewvol 128 no 4 pp 1187ndash1193 2000

[6] T N Krishnamurti C M Kishtawal Z Zhang et alldquoMultimodel ensemble forecasts for weather and seasonalclimaterdquo Journal of Climate vol 13 no 23 pp 4196ndash42162000

[7] H C Weber ldquoHurricane track prediction using a statisticalensemble of numerical modelsrdquo Monthly Weather Reviewvol 131 no 5 pp 749ndash770 2003

[8] M Ruttgers S Lee S Jeon and D You ldquoPrediction of atyphoon track using a generative adversarial network andsatellite imagesrdquo Scientific Reports vol 9 no 1pp 6057ndash6115 2019

[9] C Wang Q Xu X Li et al ldquoCNN-based tropical cyclonetrack forecasting from satellite infrared imagesrdquo in Pro-ceedings of the IEEE International Geoscience and RemoteSensing Symposium pp 5811ndash5814 Waikoloa HI USASeptember 2020

[10] S Gao P Zhao B Pan et al ldquoA nowcasting model for theprediction of typhoon tracks based on a long short termmemory neural networkrdquo Acta Oceanologica Sinica vol 37no 5 pp 8ndash12 2018

[11] J Chen M Zhong J Li D Wang T Qian and H TuldquoEffective deep attributed network representation learningwith topology adapted smoothingrdquo IEEE Transactions onCybernetics 2021

[12] S Giffard-Roisin M Yang G Charpiat C Kumler BonfantiB Kegl and C Monteleoni ldquoTropical cyclone track fore-casting using fused deep learning from aligned reanalysisdatardquo Frontiers in Big Data vol 3 p 1 2020

[13] M Moradi Kordmahalleh M Gorji Sefidmazgi andA Homaifar ldquoA sparse recurrent neural network for tra-jectory prediction of atlantic hurricanesrdquo in Proceedings of theGenetic and Evolutionary Computation Conference pp 957ndash964 Lille France July 2016

[14] S Alemany J Beltran A Perez et al ldquoPredicting hurricanetrajectories using a recurrent neural networkrdquo in Proceedingsof the irty-ird AAAI Conference on Artificial Intelligencepp 468ndash475 Honolulu HI USA January 2019

[15] R Chandra and K Dayal ldquoCooperative neuro-evolution ofElman recurrent networks for tropical cyclone wind-intensityprediction in the south pacific regionrdquo in Proceedings of the

Table 2 Results compared with the existing works

6 h 24 h 48 h 72 hOur framework 3875 6954 19661 3681Gao et al [10] 4595 10568 33254 97450Giffard-Roisin et al [12] mdash 1361 mdash mdashRuttgers et al [8] 956 mdash mdash mdashLv et al [27] mdash 15834 36176 mdash

Complexity 11

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity

Lmain

(x minus 1113954x)2

+(y minus 1113954y)2

1113969

(3)

where (x y) is the location of the typhoon at the nextmoment and (1113954x 1113954y) is the output of the predictor elongitude and latitude offset can also be used as input andthe corresponding loss will become the difference in theoffset For the auxiliary task the loss function is denoted as

Lauxiliary

(p minus 1113954p)2

1113969

(4)

where p is the central pressure of the typhoon and 1113954p is theprediction result

erefore the total loss of our framework is as follows

Ltotal αLmain +(1 minus α)Lauxiliary (5)

In this loss function α is a hyperparameter

43 Distributed Implementation To ensure that the pro-posed framework can handle big data and consequentlyimprove the efficiency of training we implemented a dis-tributed framework based on Ray Ray is a very populardistributed AI platform implemented via Python is fa-cilitates the rapidly distributed computing of the Python

Avg pooling

FC 5

3 times 3 conv 64

BNBN

ReLu

3 times 3 conv 128

BN

ReLu

BNBN

ReLu

3 times 3 conv 256

BN

ReLu

3 times 3 conv 256

BN

ReLu

3x3 conv 512

ReLu

3 times 3 conv 512

BN

ReLu

3 times 3 conv 512

BN

ReLuMax pooling

Input3 times 121 times 161

Block 1 Block 2 Block 3 Block 4

BN

7 times 7 conv 64 2padding = (3 3)

BN

ReLu

Shortcut Shortcut Shortcut Shortcut

3 times 3 conv 64 3 times 3 conv 128

hellip hellip hellip hellip

BN

ReLu

Figure 5 Details of ResNet in our framework

0deg

10degN

20degN

30degN

40degN

50degN

60degN

11600

11800

12000

12200

12400

Geo

pote

ntia

l hei

ght (

200

hPa)

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(a)

0deg

10degN

20degN

30degN

40degN

50degN

60degN

5400

5500

5600

5700

5800

Geo

pote

ntia

l hei

ght (

500

hPa)

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(b)

0deg1350

Geo

pote

ntia

l hei

ght (

850

hPa)

1400

1450

1500

1550

10degN

20degN

30degN

40degN

50degN

60degN

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(c)

0deg

10degN

20degN

30degN

40degN

50degN

60degNSp

ecifi

c hum

idity

(200

hPa)

00000

00002

00004

00006

00008

00010

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

(d)

0deg 00000 Spec

ific h

umid

ity (5

00 h

Pa)

00005000100001500020000250003000035

10degN

20degN

30degN

40degN

50degN

60degN

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W(e)

100deg

E11

0degE

120deg

E13

0degE

140deg

E15

0degE

160deg

E17

0degE

180deg

W

0000 Spec

ific h

umid

ity (8

50hP

a)

000200040006000800100012

0deg

10degN

20degN

30degN

40degN

50degN

60degN

(f )

Figure 4 Example of GH and SH (a) GH at 200 hPa (b) GH at 500 hPa (c) GH at 850 hPa (d) SH at 200 hPa (e) SH at 500 hPa (f ) SH at850 hPa

6 Complexity

code In the implementation each network structure (suchas convolution layer pooling layer and FC layer) isimplemented as a class also known as an actor in RayMultiple actors construct the entire network through thedata flow In the calculation process each calculation nodestarts multiple workers as the basis of calculation Each actoris assigned to the corresponding worker for execution In thetraining process the data flows through gRPC and sharedmemory to the corresponding worker for calculation Forexample in each ResNet after the calculation of the currentlayer is completed the data will flow to the worker of thenext layer ere is no data dependence between multipleResNets therefore parallel training can be realized

5 Experiments

51 Setup We use a real dataset to verify the effectiveness ofour framework e dataset is the Western Pacific Typhoontrack data from the JTWC (be TyphoonWarning Center theJoint Typhoon Warning Center) e dataset contains ty-phoon tracks from January 1 2001 to December 31 2005e attitude is from 0degN to 60degN and the longitude is from100degE to 180degE Statistics of the experimental setup areshown in Table 1

We use the metric of distance error (same as Lmain) toverify the effectiveness of our framework We first verify thebenefits of multitask learning technology to this frameworkNext we use different weights to discuss the relationshipbetween features and resultse framework is implementedby Python 3 and the experiments are conducted on a clusterin which each node has Intel Purley 4110 CPUs and TeslaP100 GPUs

52 Results In this section we will introduce the experi-mental results in the real-life dataset We report and analysethe results by changing the parameters en we choosesome real typhoon tracks to show our prediction results

Distance error with respect to multitask and single-tasklearning firstly we compare the results of multitasklearning (MTL) and single-task learning (STL) asshown in Figure 7 We can obverse that MTL can getbetter results than STL in most cases In the 6 h pre-diction results MTL is similar to STL However inother cases MTL can achieve about 20 performanceimprovement It proves that it is feasible to improve the

effect of track prediction by auxiliary tasks What ismore the best results in 6 h 24 h 48 h and 72 h areabout 40 km 70 km 220 km and 380 km which arebetter than most existing models It also proves theeffectiveness of our frameworkDistance error with respect to |T||T| secondly wereport the distance error with different size of input |T|e results are also shown in Figure 7 We find that |T|

has a great influence on our framework in differentcases e optimal value is 3 7 4 and 5 in 6 h 24 h48 h and 72 h As |T| becomes larger or smaller thedistance error gradually increases In the later experi-ments we selected the best value of |T| in each case toverify the effect of feature weight on the distance error

en we study the relationship between features andprediction results

Distance error with respect to wSSTwSST to study theeffect of SST we keep wGH and wSH unchanged andthen adjust the value of wSST from 01 to 10 e resultsare shown in Figure 8 We can obverse that SST willgreatly affect the results e best choice is to reducewSST as small as possibleDistance error with respect to wGHwGH to study therelationship between GH and prediction results wekeep wSST and wSH unchanged and then adjust thevalue of wGH from 01 to 10 As shown in Figure 9 wecan get the best results when wGH is set as 08 edifference between the best result and the worst resultin 6 h 24 h and 48 h is about 30 km to 100 km In 72 hthe difference could be more than 300 km An ap-propriate wGH can improve the results by 30 to 40e experimental results show that there is a strongcorrelation between GH and prediction resultsDistance error with respect to wSHwSH to study therelationship between SH and prediction results wekeep wSST and wGH unchanged and then adjust thevalue of wSH from 01 to 10 e results are shown inFigure 10 To get better resultswSH is smaller thanwGHIn 6 h and 24 h cases we can get the best results whenwSH is set as 01 In 48 h and 72 h cases it is better to setwSH as 03 An appropriate wSH can improve the resultby 40 to 50 e experimental results show that SHis also related to the prediction results but the cor-relation is less than GH

Zero

-sta

te in

itial

izat

ion

LSTM cell LSTM cell LSTM cellhellip

Task 1

Task 2

hellip

f1 f2 ft

hellip hellip

hellip

helliphellip

Figure 6 e details of multitask prediction

Complexity 7

Table 1 Statistics of the experimental setup

Region Date range Dimension of featuresAttitude Longitude January 1 2001 to December 31 2005 SST GHSH Others0degN to 60degN 100degE to 180degE 121 times 161 3 times 121 times 161 20 times 1

40

80

120

160

200

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

6 h STL6 h MTL

24 h STL24 h MTL

(a)

200

300

400

500

600

700

800

900

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

48 h STL48 h MTL

72 h STL72 h MTL

(b)

Figure 7 Results of varying |T| (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

250

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSST

(a)

0

200

400

600

800

1000

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSST

48h prediction72h prediction

(b)

Figure 8 Results of varying weight of wSST (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

8 Complexity

Case study We use some real typhoons to compare thereal tracks and the prediction results We select SaolaDamrey and Longwang that are formed in 2005 thereal tracks and 6 h prediction results are shown inFigures 11ndash13 Typhoon Saola was formed on Sep-tember 20th the average distance error of 6 h

prediction results is 4033 km Typhoon Damrey wasformed on September 21 the average distance error is4059 km the minimum error is 89 km and themaximum error is 6033 km Typhoon Longwang wasformed on September 26 the average distance error is4651 km

20

40

60

80

100

120

140

160

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WGH

(a)

100

200

300

400

500

600

700

800

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WGH

48h prediction72h prediction

(b)

Figure 9 Results of varying weight of wGH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSH

(a)

150

250

350

450

550

650

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSH

48h prediction72h prediction

(b)

Figure 10 Results of varying weight of wSH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

Complexity 9

Comparison with existing works Finally we compareour framework with several existing works [8101227]According to the previous introduction Ruttgers et al[8] introduced a GAN-based model used satelliteimages as the input and predicted locations after 6hours Gao et al [10] introduced an LSTM-basedmodel e work by Giffard-Roisin et al [12] was based

on CNN and feature fusion Lv et al [27] used the leastsquare method and FC network to predict the locationsWe still use distance error to verify the effectiveness andthe results are shown in Table 2 Compared with theseworks our framework can achieve high predictionresults especially in 48 h and 72 h cases In 72 h resultsour framework improves the accuracy by 60

20

24

28

32

36

40

136 140 144 148 152La

titud

eLongitude

Real track6 h prediction

Figure 11 6 h prediction results of Saola

16

17

18

19

20

21

112 114 116 118 120 122 124

Latit

ude

Longitude

Real track6h prediction

Figure 12 6 h prediction results of Damrey

19

20

21

22

23

24

25

26

115 120 125 130 135 140 145

Latit

ude

Longitude

Real track6h prediction

Figure 13 6 h prediction results of Longwang

10 Complexity

53 Summary In this section we verify the effect of differentparameters on the performance of our framework in the realdataset In general our framework can achieve good resultsbased on multitask and feature weighting We find that GHhas a strong correlation with the movement of typhoonsfollowed by SH and SST has the weakest correlationrough the training results the optimal prediction resultscan be obtained by selecting the appropriate parameters fordifferent scenes

6 Conclusion

In this paper we proposed a typhoon track predictionframework based on multitask learning and featureweightingWe analysed the correlation between the climaticgeographical and physical features and typhoon movementthrough the method of feature weighting We designed anetwork based on ResNet and LSTM and used a multitasklearning method to improve the prediction accuracy Weimplemented the network in a distributed platform Finallywe conducted experiments on real datasets to prove theeffectiveness of the framework In future works we willanalyse more features and use the attention mechanism toautomatically process the weight of features

Data Availability

e data are available from the corresponding author uponrequest

Conflicts of Interest

e authors declare that they have no conflicts of interest tothis work

Acknowledgments

e work was supported by the National Key RampD Programof China (Grant no 2016YFC1401902) the National NaturalScience Foundation of China (Grant no 61972077) and theLiaoNing Revitalization Talents Program (Grant noXLYC2007079)

References

[1] W Liu K Fujii Y Maruyama and F Yamazaki ldquoInundationassessment of the 2019 typhoon hagibis in Japan using multi-temporal sentinel-1 intensity imagesrdquo Remote Sensing vol 13no 4 p 639 2021

[2] J Cai Y Zhang R J Doviak Y Shrestha and P W ChanldquoDiagnosis and classification of typhoon-associated low-al-titude turbulence using HKO-TDWR radar observations and

machine learningrdquo IEEE Transactions on Geoscience andRemote Sensing vol 57 no 6 pp 3633ndash3648 2019

[3] J Li Q Zheng M Li Q Li and L Xie ldquoSpatiotemporaldistributions of ocean color elements in response to tropicalcyclone a case study of typhoon mangkhut (2018) past overthe northern south China seardquo Remote Sensing vol 13 no 4p 687 2021

[4] M Demaria MMainelli L K Shay J A Knaff and J KaplanldquoFurther improvements to the statistical hurricane intensityprediction scheme (SHIPS)rdquo Weather and Forecastingvol 20 no 4 pp 531ndash543 2005

[5] J S Goerss ldquoTropical cyclone track forecasts using an en-semble of dynamical modelsrdquo Monthly Weather Reviewvol 128 no 4 pp 1187ndash1193 2000

[6] T N Krishnamurti C M Kishtawal Z Zhang et alldquoMultimodel ensemble forecasts for weather and seasonalclimaterdquo Journal of Climate vol 13 no 23 pp 4196ndash42162000

[7] H C Weber ldquoHurricane track prediction using a statisticalensemble of numerical modelsrdquo Monthly Weather Reviewvol 131 no 5 pp 749ndash770 2003

[8] M Ruttgers S Lee S Jeon and D You ldquoPrediction of atyphoon track using a generative adversarial network andsatellite imagesrdquo Scientific Reports vol 9 no 1pp 6057ndash6115 2019

[9] C Wang Q Xu X Li et al ldquoCNN-based tropical cyclonetrack forecasting from satellite infrared imagesrdquo in Pro-ceedings of the IEEE International Geoscience and RemoteSensing Symposium pp 5811ndash5814 Waikoloa HI USASeptember 2020

[10] S Gao P Zhao B Pan et al ldquoA nowcasting model for theprediction of typhoon tracks based on a long short termmemory neural networkrdquo Acta Oceanologica Sinica vol 37no 5 pp 8ndash12 2018

[11] J Chen M Zhong J Li D Wang T Qian and H TuldquoEffective deep attributed network representation learningwith topology adapted smoothingrdquo IEEE Transactions onCybernetics 2021

[12] S Giffard-Roisin M Yang G Charpiat C Kumler BonfantiB Kegl and C Monteleoni ldquoTropical cyclone track fore-casting using fused deep learning from aligned reanalysisdatardquo Frontiers in Big Data vol 3 p 1 2020

[13] M Moradi Kordmahalleh M Gorji Sefidmazgi andA Homaifar ldquoA sparse recurrent neural network for tra-jectory prediction of atlantic hurricanesrdquo in Proceedings of theGenetic and Evolutionary Computation Conference pp 957ndash964 Lille France July 2016

[14] S Alemany J Beltran A Perez et al ldquoPredicting hurricanetrajectories using a recurrent neural networkrdquo in Proceedingsof the irty-ird AAAI Conference on Artificial Intelligencepp 468ndash475 Honolulu HI USA January 2019

[15] R Chandra and K Dayal ldquoCooperative neuro-evolution ofElman recurrent networks for tropical cyclone wind-intensityprediction in the south pacific regionrdquo in Proceedings of the

Table 2 Results compared with the existing works

6 h 24 h 48 h 72 hOur framework 3875 6954 19661 3681Gao et al [10] 4595 10568 33254 97450Giffard-Roisin et al [12] mdash 1361 mdash mdashRuttgers et al [8] 956 mdash mdash mdashLv et al [27] mdash 15834 36176 mdash

Complexity 11

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity

code In the implementation each network structure (suchas convolution layer pooling layer and FC layer) isimplemented as a class also known as an actor in RayMultiple actors construct the entire network through thedata flow In the calculation process each calculation nodestarts multiple workers as the basis of calculation Each actoris assigned to the corresponding worker for execution In thetraining process the data flows through gRPC and sharedmemory to the corresponding worker for calculation Forexample in each ResNet after the calculation of the currentlayer is completed the data will flow to the worker of thenext layer ere is no data dependence between multipleResNets therefore parallel training can be realized

5 Experiments

51 Setup We use a real dataset to verify the effectiveness ofour framework e dataset is the Western Pacific Typhoontrack data from the JTWC (be TyphoonWarning Center theJoint Typhoon Warning Center) e dataset contains ty-phoon tracks from January 1 2001 to December 31 2005e attitude is from 0degN to 60degN and the longitude is from100degE to 180degE Statistics of the experimental setup areshown in Table 1

We use the metric of distance error (same as Lmain) toverify the effectiveness of our framework We first verify thebenefits of multitask learning technology to this frameworkNext we use different weights to discuss the relationshipbetween features and resultse framework is implementedby Python 3 and the experiments are conducted on a clusterin which each node has Intel Purley 4110 CPUs and TeslaP100 GPUs

52 Results In this section we will introduce the experi-mental results in the real-life dataset We report and analysethe results by changing the parameters en we choosesome real typhoon tracks to show our prediction results

Distance error with respect to multitask and single-tasklearning firstly we compare the results of multitasklearning (MTL) and single-task learning (STL) asshown in Figure 7 We can obverse that MTL can getbetter results than STL in most cases In the 6 h pre-diction results MTL is similar to STL However inother cases MTL can achieve about 20 performanceimprovement It proves that it is feasible to improve the

effect of track prediction by auxiliary tasks What ismore the best results in 6 h 24 h 48 h and 72 h areabout 40 km 70 km 220 km and 380 km which arebetter than most existing models It also proves theeffectiveness of our frameworkDistance error with respect to |T||T| secondly wereport the distance error with different size of input |T|e results are also shown in Figure 7 We find that |T|

has a great influence on our framework in differentcases e optimal value is 3 7 4 and 5 in 6 h 24 h48 h and 72 h As |T| becomes larger or smaller thedistance error gradually increases In the later experi-ments we selected the best value of |T| in each case toverify the effect of feature weight on the distance error

en we study the relationship between features andprediction results

Distance error with respect to wSSTwSST to study theeffect of SST we keep wGH and wSH unchanged andthen adjust the value of wSST from 01 to 10 e resultsare shown in Figure 8 We can obverse that SST willgreatly affect the results e best choice is to reducewSST as small as possibleDistance error with respect to wGHwGH to study therelationship between GH and prediction results wekeep wSST and wSH unchanged and then adjust thevalue of wGH from 01 to 10 As shown in Figure 9 wecan get the best results when wGH is set as 08 edifference between the best result and the worst resultin 6 h 24 h and 48 h is about 30 km to 100 km In 72 hthe difference could be more than 300 km An ap-propriate wGH can improve the results by 30 to 40e experimental results show that there is a strongcorrelation between GH and prediction resultsDistance error with respect to wSHwSH to study therelationship between SH and prediction results wekeep wSST and wGH unchanged and then adjust thevalue of wSH from 01 to 10 e results are shown inFigure 10 To get better resultswSH is smaller thanwGHIn 6 h and 24 h cases we can get the best results whenwSH is set as 01 In 48 h and 72 h cases it is better to setwSH as 03 An appropriate wSH can improve the resultby 40 to 50 e experimental results show that SHis also related to the prediction results but the cor-relation is less than GH

Zero

-sta

te in

itial

izat

ion

LSTM cell LSTM cell LSTM cellhellip

Task 1

Task 2

hellip

f1 f2 ft

hellip hellip

hellip

helliphellip

Figure 6 e details of multitask prediction

Complexity 7

Table 1 Statistics of the experimental setup

Region Date range Dimension of featuresAttitude Longitude January 1 2001 to December 31 2005 SST GHSH Others0degN to 60degN 100degE to 180degE 121 times 161 3 times 121 times 161 20 times 1

40

80

120

160

200

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

6 h STL6 h MTL

24 h STL24 h MTL

(a)

200

300

400

500

600

700

800

900

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

48 h STL48 h MTL

72 h STL72 h MTL

(b)

Figure 7 Results of varying |T| (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

250

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSST

(a)

0

200

400

600

800

1000

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSST

48h prediction72h prediction

(b)

Figure 8 Results of varying weight of wSST (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

8 Complexity

Case study We use some real typhoons to compare thereal tracks and the prediction results We select SaolaDamrey and Longwang that are formed in 2005 thereal tracks and 6 h prediction results are shown inFigures 11ndash13 Typhoon Saola was formed on Sep-tember 20th the average distance error of 6 h

prediction results is 4033 km Typhoon Damrey wasformed on September 21 the average distance error is4059 km the minimum error is 89 km and themaximum error is 6033 km Typhoon Longwang wasformed on September 26 the average distance error is4651 km

20

40

60

80

100

120

140

160

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WGH

(a)

100

200

300

400

500

600

700

800

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WGH

48h prediction72h prediction

(b)

Figure 9 Results of varying weight of wGH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSH

(a)

150

250

350

450

550

650

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSH

48h prediction72h prediction

(b)

Figure 10 Results of varying weight of wSH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

Complexity 9

Comparison with existing works Finally we compareour framework with several existing works [8101227]According to the previous introduction Ruttgers et al[8] introduced a GAN-based model used satelliteimages as the input and predicted locations after 6hours Gao et al [10] introduced an LSTM-basedmodel e work by Giffard-Roisin et al [12] was based

on CNN and feature fusion Lv et al [27] used the leastsquare method and FC network to predict the locationsWe still use distance error to verify the effectiveness andthe results are shown in Table 2 Compared with theseworks our framework can achieve high predictionresults especially in 48 h and 72 h cases In 72 h resultsour framework improves the accuracy by 60

20

24

28

32

36

40

136 140 144 148 152La

titud

eLongitude

Real track6 h prediction

Figure 11 6 h prediction results of Saola

16

17

18

19

20

21

112 114 116 118 120 122 124

Latit

ude

Longitude

Real track6h prediction

Figure 12 6 h prediction results of Damrey

19

20

21

22

23

24

25

26

115 120 125 130 135 140 145

Latit

ude

Longitude

Real track6h prediction

Figure 13 6 h prediction results of Longwang

10 Complexity

53 Summary In this section we verify the effect of differentparameters on the performance of our framework in the realdataset In general our framework can achieve good resultsbased on multitask and feature weighting We find that GHhas a strong correlation with the movement of typhoonsfollowed by SH and SST has the weakest correlationrough the training results the optimal prediction resultscan be obtained by selecting the appropriate parameters fordifferent scenes

6 Conclusion

In this paper we proposed a typhoon track predictionframework based on multitask learning and featureweightingWe analysed the correlation between the climaticgeographical and physical features and typhoon movementthrough the method of feature weighting We designed anetwork based on ResNet and LSTM and used a multitasklearning method to improve the prediction accuracy Weimplemented the network in a distributed platform Finallywe conducted experiments on real datasets to prove theeffectiveness of the framework In future works we willanalyse more features and use the attention mechanism toautomatically process the weight of features

Data Availability

e data are available from the corresponding author uponrequest

Conflicts of Interest

e authors declare that they have no conflicts of interest tothis work

Acknowledgments

e work was supported by the National Key RampD Programof China (Grant no 2016YFC1401902) the National NaturalScience Foundation of China (Grant no 61972077) and theLiaoNing Revitalization Talents Program (Grant noXLYC2007079)

References

[1] W Liu K Fujii Y Maruyama and F Yamazaki ldquoInundationassessment of the 2019 typhoon hagibis in Japan using multi-temporal sentinel-1 intensity imagesrdquo Remote Sensing vol 13no 4 p 639 2021

[2] J Cai Y Zhang R J Doviak Y Shrestha and P W ChanldquoDiagnosis and classification of typhoon-associated low-al-titude turbulence using HKO-TDWR radar observations and

machine learningrdquo IEEE Transactions on Geoscience andRemote Sensing vol 57 no 6 pp 3633ndash3648 2019

[3] J Li Q Zheng M Li Q Li and L Xie ldquoSpatiotemporaldistributions of ocean color elements in response to tropicalcyclone a case study of typhoon mangkhut (2018) past overthe northern south China seardquo Remote Sensing vol 13 no 4p 687 2021

[4] M Demaria MMainelli L K Shay J A Knaff and J KaplanldquoFurther improvements to the statistical hurricane intensityprediction scheme (SHIPS)rdquo Weather and Forecastingvol 20 no 4 pp 531ndash543 2005

[5] J S Goerss ldquoTropical cyclone track forecasts using an en-semble of dynamical modelsrdquo Monthly Weather Reviewvol 128 no 4 pp 1187ndash1193 2000

[6] T N Krishnamurti C M Kishtawal Z Zhang et alldquoMultimodel ensemble forecasts for weather and seasonalclimaterdquo Journal of Climate vol 13 no 23 pp 4196ndash42162000

[7] H C Weber ldquoHurricane track prediction using a statisticalensemble of numerical modelsrdquo Monthly Weather Reviewvol 131 no 5 pp 749ndash770 2003

[8] M Ruttgers S Lee S Jeon and D You ldquoPrediction of atyphoon track using a generative adversarial network andsatellite imagesrdquo Scientific Reports vol 9 no 1pp 6057ndash6115 2019

[9] C Wang Q Xu X Li et al ldquoCNN-based tropical cyclonetrack forecasting from satellite infrared imagesrdquo in Pro-ceedings of the IEEE International Geoscience and RemoteSensing Symposium pp 5811ndash5814 Waikoloa HI USASeptember 2020

[10] S Gao P Zhao B Pan et al ldquoA nowcasting model for theprediction of typhoon tracks based on a long short termmemory neural networkrdquo Acta Oceanologica Sinica vol 37no 5 pp 8ndash12 2018

[11] J Chen M Zhong J Li D Wang T Qian and H TuldquoEffective deep attributed network representation learningwith topology adapted smoothingrdquo IEEE Transactions onCybernetics 2021

[12] S Giffard-Roisin M Yang G Charpiat C Kumler BonfantiB Kegl and C Monteleoni ldquoTropical cyclone track fore-casting using fused deep learning from aligned reanalysisdatardquo Frontiers in Big Data vol 3 p 1 2020

[13] M Moradi Kordmahalleh M Gorji Sefidmazgi andA Homaifar ldquoA sparse recurrent neural network for tra-jectory prediction of atlantic hurricanesrdquo in Proceedings of theGenetic and Evolutionary Computation Conference pp 957ndash964 Lille France July 2016

[14] S Alemany J Beltran A Perez et al ldquoPredicting hurricanetrajectories using a recurrent neural networkrdquo in Proceedingsof the irty-ird AAAI Conference on Artificial Intelligencepp 468ndash475 Honolulu HI USA January 2019

[15] R Chandra and K Dayal ldquoCooperative neuro-evolution ofElman recurrent networks for tropical cyclone wind-intensityprediction in the south pacific regionrdquo in Proceedings of the

Table 2 Results compared with the existing works

6 h 24 h 48 h 72 hOur framework 3875 6954 19661 3681Gao et al [10] 4595 10568 33254 97450Giffard-Roisin et al [12] mdash 1361 mdash mdashRuttgers et al [8] 956 mdash mdash mdashLv et al [27] mdash 15834 36176 mdash

Complexity 11

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity

Table 1 Statistics of the experimental setup

Region Date range Dimension of featuresAttitude Longitude January 1 2001 to December 31 2005 SST GHSH Others0degN to 60degN 100degE to 180degE 121 times 161 3 times 121 times 161 20 times 1

40

80

120

160

200

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

6 h STL6 h MTL

24 h STL24 h MTL

(a)

200

300

400

500

600

700

800

900

2 3 4 5 6 7 8

Dist

ance

erro

r (km

)

|T|

48 h STL48 h MTL

72 h STL72 h MTL

(b)

Figure 7 Results of varying |T| (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

250

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSST

(a)

0

200

400

600

800

1000

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSST

48h prediction72h prediction

(b)

Figure 8 Results of varying weight of wSST (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

8 Complexity

Case study We use some real typhoons to compare thereal tracks and the prediction results We select SaolaDamrey and Longwang that are formed in 2005 thereal tracks and 6 h prediction results are shown inFigures 11ndash13 Typhoon Saola was formed on Sep-tember 20th the average distance error of 6 h

prediction results is 4033 km Typhoon Damrey wasformed on September 21 the average distance error is4059 km the minimum error is 89 km and themaximum error is 6033 km Typhoon Longwang wasformed on September 26 the average distance error is4651 km

20

40

60

80

100

120

140

160

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WGH

(a)

100

200

300

400

500

600

700

800

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WGH

48h prediction72h prediction

(b)

Figure 9 Results of varying weight of wGH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSH

(a)

150

250

350

450

550

650

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSH

48h prediction72h prediction

(b)

Figure 10 Results of varying weight of wSH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

Complexity 9

Comparison with existing works Finally we compareour framework with several existing works [8101227]According to the previous introduction Ruttgers et al[8] introduced a GAN-based model used satelliteimages as the input and predicted locations after 6hours Gao et al [10] introduced an LSTM-basedmodel e work by Giffard-Roisin et al [12] was based

on CNN and feature fusion Lv et al [27] used the leastsquare method and FC network to predict the locationsWe still use distance error to verify the effectiveness andthe results are shown in Table 2 Compared with theseworks our framework can achieve high predictionresults especially in 48 h and 72 h cases In 72 h resultsour framework improves the accuracy by 60

20

24

28

32

36

40

136 140 144 148 152La

titud

eLongitude

Real track6 h prediction

Figure 11 6 h prediction results of Saola

16

17

18

19

20

21

112 114 116 118 120 122 124

Latit

ude

Longitude

Real track6h prediction

Figure 12 6 h prediction results of Damrey

19

20

21

22

23

24

25

26

115 120 125 130 135 140 145

Latit

ude

Longitude

Real track6h prediction

Figure 13 6 h prediction results of Longwang

10 Complexity

53 Summary In this section we verify the effect of differentparameters on the performance of our framework in the realdataset In general our framework can achieve good resultsbased on multitask and feature weighting We find that GHhas a strong correlation with the movement of typhoonsfollowed by SH and SST has the weakest correlationrough the training results the optimal prediction resultscan be obtained by selecting the appropriate parameters fordifferent scenes

6 Conclusion

In this paper we proposed a typhoon track predictionframework based on multitask learning and featureweightingWe analysed the correlation between the climaticgeographical and physical features and typhoon movementthrough the method of feature weighting We designed anetwork based on ResNet and LSTM and used a multitasklearning method to improve the prediction accuracy Weimplemented the network in a distributed platform Finallywe conducted experiments on real datasets to prove theeffectiveness of the framework In future works we willanalyse more features and use the attention mechanism toautomatically process the weight of features

Data Availability

e data are available from the corresponding author uponrequest

Conflicts of Interest

e authors declare that they have no conflicts of interest tothis work

Acknowledgments

e work was supported by the National Key RampD Programof China (Grant no 2016YFC1401902) the National NaturalScience Foundation of China (Grant no 61972077) and theLiaoNing Revitalization Talents Program (Grant noXLYC2007079)

References

[1] W Liu K Fujii Y Maruyama and F Yamazaki ldquoInundationassessment of the 2019 typhoon hagibis in Japan using multi-temporal sentinel-1 intensity imagesrdquo Remote Sensing vol 13no 4 p 639 2021

[2] J Cai Y Zhang R J Doviak Y Shrestha and P W ChanldquoDiagnosis and classification of typhoon-associated low-al-titude turbulence using HKO-TDWR radar observations and

machine learningrdquo IEEE Transactions on Geoscience andRemote Sensing vol 57 no 6 pp 3633ndash3648 2019

[3] J Li Q Zheng M Li Q Li and L Xie ldquoSpatiotemporaldistributions of ocean color elements in response to tropicalcyclone a case study of typhoon mangkhut (2018) past overthe northern south China seardquo Remote Sensing vol 13 no 4p 687 2021

[4] M Demaria MMainelli L K Shay J A Knaff and J KaplanldquoFurther improvements to the statistical hurricane intensityprediction scheme (SHIPS)rdquo Weather and Forecastingvol 20 no 4 pp 531ndash543 2005

[5] J S Goerss ldquoTropical cyclone track forecasts using an en-semble of dynamical modelsrdquo Monthly Weather Reviewvol 128 no 4 pp 1187ndash1193 2000

[6] T N Krishnamurti C M Kishtawal Z Zhang et alldquoMultimodel ensemble forecasts for weather and seasonalclimaterdquo Journal of Climate vol 13 no 23 pp 4196ndash42162000

[7] H C Weber ldquoHurricane track prediction using a statisticalensemble of numerical modelsrdquo Monthly Weather Reviewvol 131 no 5 pp 749ndash770 2003

[8] M Ruttgers S Lee S Jeon and D You ldquoPrediction of atyphoon track using a generative adversarial network andsatellite imagesrdquo Scientific Reports vol 9 no 1pp 6057ndash6115 2019

[9] C Wang Q Xu X Li et al ldquoCNN-based tropical cyclonetrack forecasting from satellite infrared imagesrdquo in Pro-ceedings of the IEEE International Geoscience and RemoteSensing Symposium pp 5811ndash5814 Waikoloa HI USASeptember 2020

[10] S Gao P Zhao B Pan et al ldquoA nowcasting model for theprediction of typhoon tracks based on a long short termmemory neural networkrdquo Acta Oceanologica Sinica vol 37no 5 pp 8ndash12 2018

[11] J Chen M Zhong J Li D Wang T Qian and H TuldquoEffective deep attributed network representation learningwith topology adapted smoothingrdquo IEEE Transactions onCybernetics 2021

[12] S Giffard-Roisin M Yang G Charpiat C Kumler BonfantiB Kegl and C Monteleoni ldquoTropical cyclone track fore-casting using fused deep learning from aligned reanalysisdatardquo Frontiers in Big Data vol 3 p 1 2020

[13] M Moradi Kordmahalleh M Gorji Sefidmazgi andA Homaifar ldquoA sparse recurrent neural network for tra-jectory prediction of atlantic hurricanesrdquo in Proceedings of theGenetic and Evolutionary Computation Conference pp 957ndash964 Lille France July 2016

[14] S Alemany J Beltran A Perez et al ldquoPredicting hurricanetrajectories using a recurrent neural networkrdquo in Proceedingsof the irty-ird AAAI Conference on Artificial Intelligencepp 468ndash475 Honolulu HI USA January 2019

[15] R Chandra and K Dayal ldquoCooperative neuro-evolution ofElman recurrent networks for tropical cyclone wind-intensityprediction in the south pacific regionrdquo in Proceedings of the

Table 2 Results compared with the existing works

6 h 24 h 48 h 72 hOur framework 3875 6954 19661 3681Gao et al [10] 4595 10568 33254 97450Giffard-Roisin et al [12] mdash 1361 mdash mdashRuttgers et al [8] 956 mdash mdash mdashLv et al [27] mdash 15834 36176 mdash

Complexity 11

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity

Case study We use some real typhoons to compare thereal tracks and the prediction results We select SaolaDamrey and Longwang that are formed in 2005 thereal tracks and 6 h prediction results are shown inFigures 11ndash13 Typhoon Saola was formed on Sep-tember 20th the average distance error of 6 h

prediction results is 4033 km Typhoon Damrey wasformed on September 21 the average distance error is4059 km the minimum error is 89 km and themaximum error is 6033 km Typhoon Longwang wasformed on September 26 the average distance error is4651 km

20

40

60

80

100

120

140

160

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WGH

(a)

100

200

300

400

500

600

700

800

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WGH

48h prediction72h prediction

(b)

Figure 9 Results of varying weight of wGH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

0

50

100

150

200

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

6h prediction24h prediction

WSH

(a)

150

250

350

450

550

650

01 02 03 04 05 06 07 08 09 1

Dist

ance

erro

r (km

)

WSH

48h prediction72h prediction

(b)

Figure 10 Results of varying weight of wSH (a) Results of 6 h and 24 h (b) Results of 48 and 72 h

Complexity 9

Comparison with existing works Finally we compareour framework with several existing works [8101227]According to the previous introduction Ruttgers et al[8] introduced a GAN-based model used satelliteimages as the input and predicted locations after 6hours Gao et al [10] introduced an LSTM-basedmodel e work by Giffard-Roisin et al [12] was based

on CNN and feature fusion Lv et al [27] used the leastsquare method and FC network to predict the locationsWe still use distance error to verify the effectiveness andthe results are shown in Table 2 Compared with theseworks our framework can achieve high predictionresults especially in 48 h and 72 h cases In 72 h resultsour framework improves the accuracy by 60

20

24

28

32

36

40

136 140 144 148 152La

titud

eLongitude

Real track6 h prediction

Figure 11 6 h prediction results of Saola

16

17

18

19

20

21

112 114 116 118 120 122 124

Latit

ude

Longitude

Real track6h prediction

Figure 12 6 h prediction results of Damrey

19

20

21

22

23

24

25

26

115 120 125 130 135 140 145

Latit

ude

Longitude

Real track6h prediction

Figure 13 6 h prediction results of Longwang

10 Complexity

53 Summary In this section we verify the effect of differentparameters on the performance of our framework in the realdataset In general our framework can achieve good resultsbased on multitask and feature weighting We find that GHhas a strong correlation with the movement of typhoonsfollowed by SH and SST has the weakest correlationrough the training results the optimal prediction resultscan be obtained by selecting the appropriate parameters fordifferent scenes

6 Conclusion

In this paper we proposed a typhoon track predictionframework based on multitask learning and featureweightingWe analysed the correlation between the climaticgeographical and physical features and typhoon movementthrough the method of feature weighting We designed anetwork based on ResNet and LSTM and used a multitasklearning method to improve the prediction accuracy Weimplemented the network in a distributed platform Finallywe conducted experiments on real datasets to prove theeffectiveness of the framework In future works we willanalyse more features and use the attention mechanism toautomatically process the weight of features

Data Availability

e data are available from the corresponding author uponrequest

Conflicts of Interest

e authors declare that they have no conflicts of interest tothis work

Acknowledgments

e work was supported by the National Key RampD Programof China (Grant no 2016YFC1401902) the National NaturalScience Foundation of China (Grant no 61972077) and theLiaoNing Revitalization Talents Program (Grant noXLYC2007079)

References

[1] W Liu K Fujii Y Maruyama and F Yamazaki ldquoInundationassessment of the 2019 typhoon hagibis in Japan using multi-temporal sentinel-1 intensity imagesrdquo Remote Sensing vol 13no 4 p 639 2021

[2] J Cai Y Zhang R J Doviak Y Shrestha and P W ChanldquoDiagnosis and classification of typhoon-associated low-al-titude turbulence using HKO-TDWR radar observations and

machine learningrdquo IEEE Transactions on Geoscience andRemote Sensing vol 57 no 6 pp 3633ndash3648 2019

[3] J Li Q Zheng M Li Q Li and L Xie ldquoSpatiotemporaldistributions of ocean color elements in response to tropicalcyclone a case study of typhoon mangkhut (2018) past overthe northern south China seardquo Remote Sensing vol 13 no 4p 687 2021

[4] M Demaria MMainelli L K Shay J A Knaff and J KaplanldquoFurther improvements to the statistical hurricane intensityprediction scheme (SHIPS)rdquo Weather and Forecastingvol 20 no 4 pp 531ndash543 2005

[5] J S Goerss ldquoTropical cyclone track forecasts using an en-semble of dynamical modelsrdquo Monthly Weather Reviewvol 128 no 4 pp 1187ndash1193 2000

[6] T N Krishnamurti C M Kishtawal Z Zhang et alldquoMultimodel ensemble forecasts for weather and seasonalclimaterdquo Journal of Climate vol 13 no 23 pp 4196ndash42162000

[7] H C Weber ldquoHurricane track prediction using a statisticalensemble of numerical modelsrdquo Monthly Weather Reviewvol 131 no 5 pp 749ndash770 2003

[8] M Ruttgers S Lee S Jeon and D You ldquoPrediction of atyphoon track using a generative adversarial network andsatellite imagesrdquo Scientific Reports vol 9 no 1pp 6057ndash6115 2019

[9] C Wang Q Xu X Li et al ldquoCNN-based tropical cyclonetrack forecasting from satellite infrared imagesrdquo in Pro-ceedings of the IEEE International Geoscience and RemoteSensing Symposium pp 5811ndash5814 Waikoloa HI USASeptember 2020

[10] S Gao P Zhao B Pan et al ldquoA nowcasting model for theprediction of typhoon tracks based on a long short termmemory neural networkrdquo Acta Oceanologica Sinica vol 37no 5 pp 8ndash12 2018

[11] J Chen M Zhong J Li D Wang T Qian and H TuldquoEffective deep attributed network representation learningwith topology adapted smoothingrdquo IEEE Transactions onCybernetics 2021

[12] S Giffard-Roisin M Yang G Charpiat C Kumler BonfantiB Kegl and C Monteleoni ldquoTropical cyclone track fore-casting using fused deep learning from aligned reanalysisdatardquo Frontiers in Big Data vol 3 p 1 2020

[13] M Moradi Kordmahalleh M Gorji Sefidmazgi andA Homaifar ldquoA sparse recurrent neural network for tra-jectory prediction of atlantic hurricanesrdquo in Proceedings of theGenetic and Evolutionary Computation Conference pp 957ndash964 Lille France July 2016

[14] S Alemany J Beltran A Perez et al ldquoPredicting hurricanetrajectories using a recurrent neural networkrdquo in Proceedingsof the irty-ird AAAI Conference on Artificial Intelligencepp 468ndash475 Honolulu HI USA January 2019

[15] R Chandra and K Dayal ldquoCooperative neuro-evolution ofElman recurrent networks for tropical cyclone wind-intensityprediction in the south pacific regionrdquo in Proceedings of the

Table 2 Results compared with the existing works

6 h 24 h 48 h 72 hOur framework 3875 6954 19661 3681Gao et al [10] 4595 10568 33254 97450Giffard-Roisin et al [12] mdash 1361 mdash mdashRuttgers et al [8] 956 mdash mdash mdashLv et al [27] mdash 15834 36176 mdash

Complexity 11

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity

Comparison with existing works Finally we compareour framework with several existing works [8101227]According to the previous introduction Ruttgers et al[8] introduced a GAN-based model used satelliteimages as the input and predicted locations after 6hours Gao et al [10] introduced an LSTM-basedmodel e work by Giffard-Roisin et al [12] was based

on CNN and feature fusion Lv et al [27] used the leastsquare method and FC network to predict the locationsWe still use distance error to verify the effectiveness andthe results are shown in Table 2 Compared with theseworks our framework can achieve high predictionresults especially in 48 h and 72 h cases In 72 h resultsour framework improves the accuracy by 60

20

24

28

32

36

40

136 140 144 148 152La

titud

eLongitude

Real track6 h prediction

Figure 11 6 h prediction results of Saola

16

17

18

19

20

21

112 114 116 118 120 122 124

Latit

ude

Longitude

Real track6h prediction

Figure 12 6 h prediction results of Damrey

19

20

21

22

23

24

25

26

115 120 125 130 135 140 145

Latit

ude

Longitude

Real track6h prediction

Figure 13 6 h prediction results of Longwang

10 Complexity

53 Summary In this section we verify the effect of differentparameters on the performance of our framework in the realdataset In general our framework can achieve good resultsbased on multitask and feature weighting We find that GHhas a strong correlation with the movement of typhoonsfollowed by SH and SST has the weakest correlationrough the training results the optimal prediction resultscan be obtained by selecting the appropriate parameters fordifferent scenes

6 Conclusion

In this paper we proposed a typhoon track predictionframework based on multitask learning and featureweightingWe analysed the correlation between the climaticgeographical and physical features and typhoon movementthrough the method of feature weighting We designed anetwork based on ResNet and LSTM and used a multitasklearning method to improve the prediction accuracy Weimplemented the network in a distributed platform Finallywe conducted experiments on real datasets to prove theeffectiveness of the framework In future works we willanalyse more features and use the attention mechanism toautomatically process the weight of features

Data Availability

e data are available from the corresponding author uponrequest

Conflicts of Interest

e authors declare that they have no conflicts of interest tothis work

Acknowledgments

e work was supported by the National Key RampD Programof China (Grant no 2016YFC1401902) the National NaturalScience Foundation of China (Grant no 61972077) and theLiaoNing Revitalization Talents Program (Grant noXLYC2007079)

References

[1] W Liu K Fujii Y Maruyama and F Yamazaki ldquoInundationassessment of the 2019 typhoon hagibis in Japan using multi-temporal sentinel-1 intensity imagesrdquo Remote Sensing vol 13no 4 p 639 2021

[2] J Cai Y Zhang R J Doviak Y Shrestha and P W ChanldquoDiagnosis and classification of typhoon-associated low-al-titude turbulence using HKO-TDWR radar observations and

machine learningrdquo IEEE Transactions on Geoscience andRemote Sensing vol 57 no 6 pp 3633ndash3648 2019

[3] J Li Q Zheng M Li Q Li and L Xie ldquoSpatiotemporaldistributions of ocean color elements in response to tropicalcyclone a case study of typhoon mangkhut (2018) past overthe northern south China seardquo Remote Sensing vol 13 no 4p 687 2021

[4] M Demaria MMainelli L K Shay J A Knaff and J KaplanldquoFurther improvements to the statistical hurricane intensityprediction scheme (SHIPS)rdquo Weather and Forecastingvol 20 no 4 pp 531ndash543 2005

[5] J S Goerss ldquoTropical cyclone track forecasts using an en-semble of dynamical modelsrdquo Monthly Weather Reviewvol 128 no 4 pp 1187ndash1193 2000

[6] T N Krishnamurti C M Kishtawal Z Zhang et alldquoMultimodel ensemble forecasts for weather and seasonalclimaterdquo Journal of Climate vol 13 no 23 pp 4196ndash42162000

[7] H C Weber ldquoHurricane track prediction using a statisticalensemble of numerical modelsrdquo Monthly Weather Reviewvol 131 no 5 pp 749ndash770 2003

[8] M Ruttgers S Lee S Jeon and D You ldquoPrediction of atyphoon track using a generative adversarial network andsatellite imagesrdquo Scientific Reports vol 9 no 1pp 6057ndash6115 2019

[9] C Wang Q Xu X Li et al ldquoCNN-based tropical cyclonetrack forecasting from satellite infrared imagesrdquo in Pro-ceedings of the IEEE International Geoscience and RemoteSensing Symposium pp 5811ndash5814 Waikoloa HI USASeptember 2020

[10] S Gao P Zhao B Pan et al ldquoA nowcasting model for theprediction of typhoon tracks based on a long short termmemory neural networkrdquo Acta Oceanologica Sinica vol 37no 5 pp 8ndash12 2018

[11] J Chen M Zhong J Li D Wang T Qian and H TuldquoEffective deep attributed network representation learningwith topology adapted smoothingrdquo IEEE Transactions onCybernetics 2021

[12] S Giffard-Roisin M Yang G Charpiat C Kumler BonfantiB Kegl and C Monteleoni ldquoTropical cyclone track fore-casting using fused deep learning from aligned reanalysisdatardquo Frontiers in Big Data vol 3 p 1 2020

[13] M Moradi Kordmahalleh M Gorji Sefidmazgi andA Homaifar ldquoA sparse recurrent neural network for tra-jectory prediction of atlantic hurricanesrdquo in Proceedings of theGenetic and Evolutionary Computation Conference pp 957ndash964 Lille France July 2016

[14] S Alemany J Beltran A Perez et al ldquoPredicting hurricanetrajectories using a recurrent neural networkrdquo in Proceedingsof the irty-ird AAAI Conference on Artificial Intelligencepp 468ndash475 Honolulu HI USA January 2019

[15] R Chandra and K Dayal ldquoCooperative neuro-evolution ofElman recurrent networks for tropical cyclone wind-intensityprediction in the south pacific regionrdquo in Proceedings of the

Table 2 Results compared with the existing works

6 h 24 h 48 h 72 hOur framework 3875 6954 19661 3681Gao et al [10] 4595 10568 33254 97450Giffard-Roisin et al [12] mdash 1361 mdash mdashRuttgers et al [8] 956 mdash mdash mdashLv et al [27] mdash 15834 36176 mdash

Complexity 11

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity

53 Summary In this section we verify the effect of differentparameters on the performance of our framework in the realdataset In general our framework can achieve good resultsbased on multitask and feature weighting We find that GHhas a strong correlation with the movement of typhoonsfollowed by SH and SST has the weakest correlationrough the training results the optimal prediction resultscan be obtained by selecting the appropriate parameters fordifferent scenes

6 Conclusion

In this paper we proposed a typhoon track predictionframework based on multitask learning and featureweightingWe analysed the correlation between the climaticgeographical and physical features and typhoon movementthrough the method of feature weighting We designed anetwork based on ResNet and LSTM and used a multitasklearning method to improve the prediction accuracy Weimplemented the network in a distributed platform Finallywe conducted experiments on real datasets to prove theeffectiveness of the framework In future works we willanalyse more features and use the attention mechanism toautomatically process the weight of features

Data Availability

e data are available from the corresponding author uponrequest

Conflicts of Interest

e authors declare that they have no conflicts of interest tothis work

Acknowledgments

e work was supported by the National Key RampD Programof China (Grant no 2016YFC1401902) the National NaturalScience Foundation of China (Grant no 61972077) and theLiaoNing Revitalization Talents Program (Grant noXLYC2007079)

References

[1] W Liu K Fujii Y Maruyama and F Yamazaki ldquoInundationassessment of the 2019 typhoon hagibis in Japan using multi-temporal sentinel-1 intensity imagesrdquo Remote Sensing vol 13no 4 p 639 2021

[2] J Cai Y Zhang R J Doviak Y Shrestha and P W ChanldquoDiagnosis and classification of typhoon-associated low-al-titude turbulence using HKO-TDWR radar observations and

machine learningrdquo IEEE Transactions on Geoscience andRemote Sensing vol 57 no 6 pp 3633ndash3648 2019

[3] J Li Q Zheng M Li Q Li and L Xie ldquoSpatiotemporaldistributions of ocean color elements in response to tropicalcyclone a case study of typhoon mangkhut (2018) past overthe northern south China seardquo Remote Sensing vol 13 no 4p 687 2021

[4] M Demaria MMainelli L K Shay J A Knaff and J KaplanldquoFurther improvements to the statistical hurricane intensityprediction scheme (SHIPS)rdquo Weather and Forecastingvol 20 no 4 pp 531ndash543 2005

[5] J S Goerss ldquoTropical cyclone track forecasts using an en-semble of dynamical modelsrdquo Monthly Weather Reviewvol 128 no 4 pp 1187ndash1193 2000

[6] T N Krishnamurti C M Kishtawal Z Zhang et alldquoMultimodel ensemble forecasts for weather and seasonalclimaterdquo Journal of Climate vol 13 no 23 pp 4196ndash42162000

[7] H C Weber ldquoHurricane track prediction using a statisticalensemble of numerical modelsrdquo Monthly Weather Reviewvol 131 no 5 pp 749ndash770 2003

[8] M Ruttgers S Lee S Jeon and D You ldquoPrediction of atyphoon track using a generative adversarial network andsatellite imagesrdquo Scientific Reports vol 9 no 1pp 6057ndash6115 2019

[9] C Wang Q Xu X Li et al ldquoCNN-based tropical cyclonetrack forecasting from satellite infrared imagesrdquo in Pro-ceedings of the IEEE International Geoscience and RemoteSensing Symposium pp 5811ndash5814 Waikoloa HI USASeptember 2020

[10] S Gao P Zhao B Pan et al ldquoA nowcasting model for theprediction of typhoon tracks based on a long short termmemory neural networkrdquo Acta Oceanologica Sinica vol 37no 5 pp 8ndash12 2018

[11] J Chen M Zhong J Li D Wang T Qian and H TuldquoEffective deep attributed network representation learningwith topology adapted smoothingrdquo IEEE Transactions onCybernetics 2021

[12] S Giffard-Roisin M Yang G Charpiat C Kumler BonfantiB Kegl and C Monteleoni ldquoTropical cyclone track fore-casting using fused deep learning from aligned reanalysisdatardquo Frontiers in Big Data vol 3 p 1 2020

[13] M Moradi Kordmahalleh M Gorji Sefidmazgi andA Homaifar ldquoA sparse recurrent neural network for tra-jectory prediction of atlantic hurricanesrdquo in Proceedings of theGenetic and Evolutionary Computation Conference pp 957ndash964 Lille France July 2016

[14] S Alemany J Beltran A Perez et al ldquoPredicting hurricanetrajectories using a recurrent neural networkrdquo in Proceedingsof the irty-ird AAAI Conference on Artificial Intelligencepp 468ndash475 Honolulu HI USA January 2019

[15] R Chandra and K Dayal ldquoCooperative neuro-evolution ofElman recurrent networks for tropical cyclone wind-intensityprediction in the south pacific regionrdquo in Proceedings of the

Table 2 Results compared with the existing works

6 h 24 h 48 h 72 hOur framework 3875 6954 19661 3681Gao et al [10] 4595 10568 33254 97450Giffard-Roisin et al [12] mdash 1361 mdash mdashRuttgers et al [8] 956 mdash mdash mdashLv et al [27] mdash 15834 36176 mdash

Complexity 11

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity

IEEE Congress on Evolutionary Computation (CEC)pp 1784ndash1791 Sendai Japan May 2015

[16] R Chandra K Dayal and N Rollings ldquoApplication of co-operative neuro-evolution of Elman recurrent networks for atwo-dimensional cyclone track prediction for the South Pa-cific regionrdquo in Proceedings of the International Joint Con-ference on Neural Networks (IJCNN) pp 1ndash8 KillarneyIreland July 2015

[17] J Lian P Dong Y Zhang J Pan and K Liu ldquoA novel data-driven tropical cyclone track prediction model based on CNNand GRU with multi-dimensional feature selectionrdquo IEEEAccess vol 8 pp 97114ndash97128 2020

[18] S Kim H Kim J Lee et al ldquoDeep-hurricane-tracker trackingand forecasting extreme climate eventsrdquo in Proceedings of theWinter Conference on Applications of Computer Vision(WACV) pp 1761ndash1769 Waikoloa HI USA January 2019

[19] R Chandra ldquoDynamic cyclone wind-intensity predictionusing co-evolutionary multi-task learningrdquo in Proceedings ofthe International Conference on Neural Information Process-ing pp 618ndash627 Guangzhou China November 2017

[20] R Chandra Y-S Ong and C-K Goh ldquoCo-evolutionarymulti-task learning for dynamic time series predictionrdquoApplied Soft Computing vol 70 pp 576ndash589 2018

[21] A Mukherjee and P Mitra ldquoJoint learning for cyclone tracknowcastingrdquo in Proceedings of the ECMLPKDD CEURWorkshop Ghent Belgium September 2020

[22] A Krizhevsky I Sutskever and G E Hinton ldquoImagenetclassification with deep convolutional neural networksrdquoAdvances in Neural Information Processing Systems vol 25pp 1097ndash1105 2012

[23] K He X Zhang S Ren et al ldquoDeep residual learning forimage recognitionrdquo in Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition (CVPR) pp 770ndash778 Las Vegas NV USA June 2016

[24] S Hochreiter and J Schmidhuber ldquoLong short-term mem-oryrdquo Neural Computation vol 9 no 8 pp 1735ndash1780 1997

[25] K-H ung and C-Y Wee ldquoA brief review on multi-tasklearningrdquo Multimedia Tools and Applications vol 77 no 22pp 29705ndash29725 2018

[26] K Chen ldquoCalculation of the maximum wind speed of ty-phoon in the western pacificrdquo Marine Science Bulletin 1985

[27] Q P Lv J Luo K Zhu et al ldquoExperiments on predictingtracks of tropical cyclones based on artificial neural networkrdquoGuangdong Meteorology pp 19ndash22 2009

12 Complexity