A WAVELET TRANSFORMATION-GENETIC ALGORITHM-ARTIFICIAL NEURAL NETWORK COMBINED MODEL FOR PRECIPITATION FORECASTING

Abstract

Black box models are one of the most common hydrological models in order to make predictions of hydrological variables such as precipitation and stream flow. In this study, performance of a combined model which consists of wavelet transformation, genetic algorithm and artificial neural network  (WGANN) were tested for prediction of monthly precipitation by using North Atlantic Oscillation (NAO) index, Southern Oscillation (SO) index and precipitation data as input in the model. The case study was carried out for Antalya which is located in Mediterranean region of Turkey. As a result, it was attained that WGANN model performed more successful than usual artificial neural network (ANN), multiple linear regression (MLR) and genetic algorithm-artificial neural network (GANN) models.

Keywords

• Artificial neural network,genetic algorithm,precipitation,regression,wavelet transformation

References

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