Application of artificial neural networks on drought prediction in Yazd (Central Iran)

In recent decades artificial neural networks (ANNs) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. This paper presents the application of artificial neural networks to predict drought in Yazd meteorological station. In this research, different architectures of artificial neural networks as well as various combinations of meteorological parameters including 3-year precipitation moving average, maximum temperatures, mean temperatures, relative humidity, mean wind speed, direction of prevalent wind and evaporation from 1966 to 2000, have been used as inputs of the models. According to the results taken from this research, dynamic structures of artificial neural networks including Recurrent Network (RN) and Time Lag Recurrent Network (TLRN) showed better performance for this application (due to higher accuracy of its out puts). Finally TLRN network with only one hidden layer and hyperbolic tangent transfer function was the most appropriate model structure to predict 3-year moving average precipitation of the next year. In facts, by prediction of the precipitation 12 months before its occurrence, it is possible to evaluate drought characteristics in advance. Results indicated that the combination of precipitation and maximum temperature are the most suitable inputs of the models to get the most outputs accuracy. In general, it was found that ANN is an efficient tool to model and predict drought events.

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