MODELING AND PREDICTION OF “EL NIÑO” PHENOMENON IN PIURA CITY USING ARTIFICIAL NEURONAL NETWORKS

(1) Miguel Jiménez Carrión, (2) Flabio Gutiérrez Segura, (3) Jorge Celi Pinzón(1,3) Department of Industrial Engineering (2) Department of Mathematics

Universidad Nacional de Piura (UNP) - PERU

INTRODUCTIONThis work focuses on the El Niño phenomenon, one ofthe most irregular and singular anomalies existing in theplanet which is formed in the Pacific Ocean involving theentire planet. In Peru, especially, on the north area, suchas Piura and other cities have constantly suffered thedevastating consequences of this phenomenon.The meteorological prediction in Peru is commonlyrealized with numerical models based on partialdifferential equations, however these models presentstrong problems due to non-linearity, and in fact, we canobserve that this kind of models do not contemplatesome variables of the reality like uncertainty. This lackdoes not allow a reliable prediction of the occurrence of“El Niño” phenomenon, in consequence it is necessary toimprove and strengthen the process involved for a betterweather forecasting.

METHODS

1.- Data collection: 1979 to 2015 (444 months):

• Sea surface temperatures (°C) of the zones Niño 1 + 2, Niño 3, Niño 4 and Niño 3.4

• Speed of the trade winds (m / s) in the zone 165 °W - 110 °W

• Precipitation at Miraflores weather station(campus UNP, Latitude: 5º10’00.0” S, longitude: 80º36’51.0” W, Altitude: 30 MASL)

Niño 3.4

Niño 3Niño 4

2.- Statistical treatment of data:

Pearson correlation was used to verify that there is arelation between the dependent variable(Precipitation) and the independent variables.

NNA as a tool for getting the time series of Sea SurfaceTemperature (SST)

NNA as a tool for getting the time series of TradeWinds Speed (TWS)

Net(series SST)=g(delay,neurons)experiments:delay = 4, 8, 12; months to predict the one that follows.neuronas = 5, 15, 25; neurons in the hidden layer.

The best NNA (lower mean square error (mse))

Net(series SST)=g(12,25)

Net(Niño)=f(SST,TWS, NHL1, NHL2,epochs)experiments:NHL1 : 4, 7, 10; neurons in the first hidden layer.NHL2 : 7, 14, 21; neurons in the second hidden layerepochs: 150, 200, 250; iterations that the algorithm repeats

The best NNA

Net(Niño)=f(SST, TWS, 10,14,250)

Net(series TWS)=h(delay,neurons)experiments:delay = 4, 8, 12; months to predict the one that follows.neuronas = 8, 16, 24; number of neurons in the hidden layer.

The best NNA

12 months delay, 2 neurons in the first hidden layerand 24 neurons in the second hidden layer.

NNA prediction of “El Niño” phenomenon (precipitation)

A multi-layer perceptron was used. The data was

normalized to the interval [-1,1]. Learning algorithm:

Levenberg-Marquardt. Activation function: the

hyperbolic tangent. Training data: 70%. Validation

:15%. Test: 15%.

CONCLUSIONS

1) ANNs was able to predict six months of precipitation inPiura, with a MSE of 0.000003 and an absolute average errorof 2.21%

2) ANN was able to predict with high precision the time inwhich precipitations were greater than 80 mm. and withlower precisión the bellow levels.

3) For better results we suggest to work with all weatherstations around Piura city.

3.- Design of Artificial Neural Networks (ANN)

RESULTS AND DISCUSSION

REFERENCES • Abbot, J. Forecasting extreme monthly rainfall events in regions of

Queensland, Australia using artificial neural networks, International Journal

of Sustainable Development and Planning, 12(07), 1117-1131 (2017).

• Cabrera, J., Validation of TRMM Daily Precipitation Data for Extreme

Events Analysis. The Case of Piura Watershed in Peru, Procedia

Engineering, 154, 154-157 (2016).

• NOAA, Climate Prediction Center, of National Oceanic and Atmospheric

Administration (2015)