application of optimization technique in estimation … · 2018-09-29 · application of...

APPLICATION OF OPTIMIZATIONTECHNIQUE IN ESTIMATION OFEVAPOTRANSPIRATION FORONION CROP IN SEMI-ARID

REGION

Lavanya M B1,Surendra H J2, Kavya B M3

Department of Civil Engineering,1Sri Venkateshwara College of Engineering, Bangalore,

2ATRIA Institute of Technology, Bangalore,3BGS Institute of Technology, Mandya

[email protected],

[email protected],[email protected]

June 26, 2018

Abstract

In this work, Mamdani Fuzzy inference method was ap-plied to evaluate the actual Evapotranspiration for onioncrop in the semi-arid region of Chitradurga district in thestate of Karnataka, India. In chitradurga Onion is a lo-cal crop having the growth rate 90-120days. A reliableand accurate estimation of Evapotranspiration is requiredfor proper water balance and to define water-crop require-ment.For this purpose, Lysimeter was set up in the fieldusing three different soil layers of specific gravity 2.30. Herean attempt had been made to estimate daily evapotranspira-tion for onion crop, for which 1000ml of water was added on

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daily basis and corresponding percolation was observed tofind the potential water balance deficit and surplus. Fuzzyapproach of estimation was carried out using three inputsand three fuzzy set for triangular membership function withCentroid defuzzification and this approach was comparedwith experimental one. The method found to be simpleand reliable to estimate the evapotranspiration in semi-aridregion.

Keywords: Evapotranspiration, Fuzzy Logic, Fuzzy Set,Lysimeter, onion crop

1 INTRODUCTION

In the context of climatic change, water resources for agriculturalactivities are decreasing with respect to time and space, in manyparts of the world with more emphasis in the arid and semi-aridregions. Due to increase in anthropogenic activities, increase inpopulation, rapid industrialization and increase in growing of cropsunder limited irrigation, Demand for water also increasing. Around,85

Excessive evaporation is another regular feature of the centralKarnataka due to higher amount of incoming radiation, higher windspeed, greater vapor pressure gradient in the overlying atmosphereand rise in temperature. Consequently the evaporation rate in-creases and the excess water are lost to the atmosphere from thesoil surface in the form of water. Therefore optimizing irrigationand economizing irrigation water use gains major importance in thestudy area. Consumptive use is a term which represents the lossof water by evapotranspiration and water used by the plant for itsmetabolic activities. It can be used in the same sense of evapo-transpiration, since the water used in actual metabolic process isinsignificant. The evapotranspiration depends upon the climaticconditions, characteristics of the plants and agricultural practices.With the knowledge of the water requirement of the crop, we canutilize the available water efficiently; hence it is necessary to esti-mate the evapotranspiration.

Many methods have been proposed for the estimation of evapo-transpiration using empirical and other technique [1]. But, presentlysimeter method was simple in construction and assessment. For

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this purpose soil test was done using core cutter apparatus and spe-cific gravity of soil was found to be 2.30. Three different soil layerswere prepared in the lysimeter. Water was added on daily basiswith a quantity of 1000ml and correspondingly percolated valuewas found to find out potential water balance surplus or deficit.Also Fuzzy approach was used to estimate the actual evapotran-spiration and method is found to be reliable for different crop insemi-arid regions.

To address complex and highly nonlinear type of data, soft com-puting technique provides an effective approach. Soft computingis a data-driven technique consists of fuzzy logic, neural comput-ing, machine learning and probabilistic reasoning. Soft computingtechniques are capable for analyzing stationary and non-stationaryseries, due to their good tolerance to uncertainty, partial truth andimprecision. The guiding principle of the soft computing is to ex-ploit the tolerance for imprecision, uncertainty and partial truth toachieve tractability, robustness and low solution cost. Soft com-puting techniques like fuzzy logic, artificial neural networks andgenetic algorithms have been recognized as attractive alternativesto the standard and well established hard computing paradigms.

Many methods have been applied for the estimation of Evapo-transpiration. Some of them are found to be useful for the analysis.The daily values of reference crop evapotranspiration (ETr) wereestimated by Penman-Monteith method for 32 years [1]. The studyof evaporation and transpiration components of evapotranspirationand to estimate their contributions in semi-arid regions character-ized by shallow groundwater table[2]. Analyzing the evaporationand evapotranspiration measured on the caldarusani lake and theirinfluences on the lakes water volume[5]. Many soft computing tech-niques were employed to estimate the water consumption underdifferent climatic conditions [6, 7, 8, and 9]. Numerical simula-tion of groundwater in this basin (Baoyang irrigation area) usingFAO Penman-Monteith method under all circumstances[10]. Iden-tifying the factors controlling the variability of Evapotranspiration(including associated atmospheric circulations)[11]. Relation be-tween pan evaporation and actual evapotranspiration by using dif-ferent types of pan evaporation apparatus [12]. Modified Penman-Monteith method to evaluate the reference evapotranspiration foreffective planning and management [13]. Different Evapotranspira-

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tion equations were calibrated and used on river Delta region [14].Approximately 1oC increase in temperature could increase ETo by30 mm per year [18]. Estimation of reference evapotranspiration(ETo) using the Multivariate Empirical Mode Decomposition [19].

In this research work, fuzzy logic method is used to estimate theactual evapotranspiration using rainfall, average temperature andsunshine hour as inputs. Fuzzy logic technique are used with dif-ferent rules, membership criteria and Fuzzy set. Based on accuracyof the developed model, optimum number of rules and Best mem-bership function are selected. For this purpose Mat lab softwarewith fuzzy tool and Graphical user interface are used. The modelswere trained based on climatic data to a certain period and cor-responding estimated models were developed for the same period.This work highlights the importance of fuzzy inference system inestimating the actual evapotranspioration.

2 STUDY AREA

The present study area covers the Hosadurga located at 140N to76.50E .It is located near the fast developing Western section ofChitradurga, about 45 kilometers from Chitradurga city.The sum-mer season lasts from March to mid-May, with a temperature inthe range 300-360. At the end of May, the monsoon season startsand lasts until the end of October.The physical features of Chi-tradurga District are such that Precipitation is confined to the pe-riod between April to October. Rainfall ranging from 500 to 600mm approximately.The type of irrigation used is mainly well irri-gation (bore well) and various crops like onion, arecanut, banana,pomegranate, flower, vegetables etc are grown. Onion is one of themajor crops of Chitradurga with growth rate 90 to 120 days. Dur-ing summer months the crops are invariably subjected to droughtcondition. Therefore, it becomes increasingly important to opti-mize the irrigation needs for the crop in semi-arid regions. Figure1 shows the location of study area (hosadurga) in the chitradurgadistrict of Karnataka state. Variations of temperature (minimum,maximum and average) are shown in the figure 2.

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Figure 1: Location map (Hosadurga)

Figure 2: Variation of Temperature (maximum, minimum, aveargein the year 2016)

3 METHODOLOGY

A) Construction of LysimeterI) Materials

The tank is made up of Plastic which is cylindrical in shapeand is having a diameter of 30cm and depth of 30cm from surfaceof soil. A small outlet opening is provided near the base to collectthe percolated water into a closed container.

The receiving container was a 5 liter air tight container. The

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size of the tank and runoff drainage pipe will determine the depthrequired for the receiver. The receiver must be deeper than thetank and must be capable of holding the largest rainfall that canbe expected at the site.II) Installation

Lysimeter was taken to the field and then it was filled with somegravel of different sizes are filled up to certain level to drain perco-lated water easily.Heavy clay or peaty soil is not suitable. Local soilwhich is lateritic soil was filled in the lysimeter up to the desiredlevel, and then the onion plant is grouted into the soil.

Figure 3: Lysimeter Setup in the field

B) Fuzzy-Logic methodIn this method the evapotranspiration will be estimated and

compared with the values obtained by Lysimeter method .Thesevalues will be compared with Climatic variables and then a set ofrules will be given to the Relation between the input and outputvalues from which the best result are obtained. In this experimentwe have given 16 set of rules, out of these 16 rules, firing rulewill desire the particular output. In this analysis three climaticvariables like rainfall, temperature and sunshine hours are used.Input and output combination used in Fuzzy analysis is representedin the table1. Methodology adopted in the mamdani fuzzy inferencesystem is shown in the figure 4, which consist of four importantsteps such as Fuzzification, fuzzy rule base, Fuzzy output engineand defuzzification.

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Figure 4: Fuzzy-Inference Methodology

4 RESULTS AND DISCUSSION

The results of developed fuzzy model using three input combina-tion were represented in the table 2, using 16 rules criteria andthree fuzzy set. Using this method, one month (day wise) evapo-transpiration was estimated for the onion crop in semi-arid region.From the results it is observed that, average value of Evapotran-spiration varying from 6 to 7mm per day, due to vary in the inputcombination. Since input rule combination for input variables arecloser, hence closer value are obtained in the results. Input andoutput combination used in this approach is shown in the figure 5.Rule viewer for the Fuzzy output is shown in the figure 6. Surfaceviewer for the entire process of this approach is shown in the figure7.

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Figure 5: Fuzzy inference menu with 3 inputs and 1 output

Figure 6: Rule viewer Output for different Input Combination

Figure 7: Surface Viewer of Fuzzy method with Input and Outputcombination

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5 CONCLUSION

Among different fuzzy set adopted like two, three and five formonthly data set, three number of fuzzy set performed better com-pare to two number of fuzzy set and five number of fuzzy set. Basedon three fuzzy set and 16 rules criteria, average value for daily Evap-otranspiration obtained was found to be 7.412mm/day.

References

[1] Deodas T. Meshram, S. D. Gorantiwar, N. V. Singh and R. K.Pal (2014), SARIMA model for generation and forecasting ofpomegranate (PunicagranatumL.) Evapotranspiration of Sola-pur district of Maharashtra, India.2nd International conferenceon Agricultural Horticultural Sciences Feb 03-05 2014.

[2] E.Balugani, M.W. Lubczynski, L.Reyes-Acosta, C.van der Tol,A.P.Francis,K.Metseler(2017) Ground water and unsaturatedzone evaporation and transpiration in semi-arid open goodland. Journal of Hydrology 547 (2017) 5466.

[3] El-BablyA.Z(2003) Estimation of evapotranspiration usingstatistical model, Bari :CIHEAM Options Mditerranennes :Series A. SeminariesMediterraneans; n. 57 pages 441-449.

[4] Frederik Schrader, Wolfgang Durnera, Johann Fank, Sebas-tian Gebler,ThomasPtz, Matthias Hannes, Ute Wollschlger(2013), Estimating precipitation and actual evapotranspirationfrom precision lysimeter measurements, Procedia Environmen-tal Sciences 19 (2013) 543 552.

[5] Florentina-iulianastan*, GianinaNeculau, LilianaZaharia ,Gabriela Ioana-Toroimac, SorinMihalache(2016) Study onevaporation and evapotranspiration measured on CaldarusaniLake(Romania). Procedia Environmental Sciences 32 (2016)281 289.

[6] H J Surendra., Darshan H T.,Gagan B R., LikithRaj.,Shreyas Gowda KJ(2017). Estimation of Evapotran-spiration for Onion crop in semi-arid region: Ex-perimental Field setup using Lysimeter. Science PG

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publication, DOI:10.11648/j.urp.20180301.11 ISSN:2575-1689(Print);ISSN:2575-1697(Online),2018;Vol3,No:1,pp.1-5

[7] H.J.Surendra, Deka P C and Kavya B M (2017), Im-proved Framework for modeling Municipal Residential Wa-ter Consumption Estimation using Wavelet-Mamdani Fuzzyapproach. International Journal of Advance research in sci-ence and engineering, Vol.no6, Issue.No8, ISSN(O):2319-8354,ISSN(P):2319-8346 August2017

[8] H.J. Surendra, Paresh Chandra Deka (2012),Effects of Statisti-cal Properties of Data Set in Predicting Performance of VariousArtificial Intelligence Techniques for Urban Water Consump-tion Time Series. International Journal of Civil Engineeringand Technology, volume3, Issue-2, July-December (2012), PP-426-436.

[9] H.J. Ningaraju, Ganesh Kumar S.B and H.J.Surendra (2016),Estimation of Runoff Using SCS-CN and GIS method in un-gauged watershed: A case study of Kharadya mill watershed,India. International Journal of Advanced Engineering Researchand Science ISSN:2349-6495.

[10] Hussein. Ahmed, Junmin Liu (2013), Evaluating ReferenceCrop Evapotranspiration (ETo) in the Centre of GuanzhongBasin, Engineering, 2013, 5, 459-468.

[11] Hugo g. Hidalgo,Daniel r. Cayan,Michael d. Dettinger (2015),Sources of Variability of Evapotranspiration in California,Journal of hydrometeorology Volume 6.

[12] HongchaoZuo, Bolong Chen, Shixin Wang, Yang Guo, BinZuo,Liyang Wu, XiaoqingGao(2016) Observational study oncomplementary relationship between pan evaporation andevapotranspiration and its variation with pan. Agriculturaland Forest Meteorology 222 (2016) 19.

[13] Isikwue, C. Bernadette, Audu, O. Moses1, Isikwue, O. Martin.(2014), Evaluation ofEvapotranspiration using FAO Penman-Monteith Method in Kano Nigeria.International Journal of Sci-ence and Technology Volume 3 No. 11.

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[14] Koffi Djaman,HousseinTabari, Alpha B.Balde, LamineDiop,Koichi Futakuchi,SuatIrmak(2016) Analysis, calibration andvalidation of evapotranspiration models to predict grass-reference evapotranspiration in the Senegal River delta.Journalof Hydrology: Regional Studies 8 (2016) 8294.

[15] M.D.Mata, K.A.Salunke, P.PBhangale (2014), Evaluation ofevapotranspiration,International Journal of Research in En-gineering and Technology Volume: 03, Special Issue: 09,NCETCE-2014.

[16] M.A.Salama,Kh.MYousef,A.Z.Mostafa(2015) Simple equationfor estimating actual evapotranspiration using heat units forwheat in arid regions.Journal of radiation research and appliedsciences 8 (2015) 418 -427.

[17] Othoman ALKAEED , Clariza FLORES , Kenji JINNOand Atsushi TSUTSUMI (2006),Comparison of Several Ref-erence Evapotranspiration Methods for Itoshima PeninsulaArea,Fukuoka, Japan, Memoirs of the Faculty of Engineering,Kyushu University, Vol. 66, No.1.

[18] S .Shekhar (2012), Study of Evapotranspiration in Context ofChanging Climate.Journal of Mechanical and Civil Engineer-ing 2278-1684 Volume 2, Issue 6 (Sep-Oct 2012), PP 13-16

[19] S.Adarsh. SulaimanSanah, K.K.Murshide, P.Nooramol(2017)Scale dependent prediction of reference evapotranspirationmulti-variateempherical mode decomposition .Ain Shams En-gineering Journal (2017) .

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