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Increases in greenhouse gases over the last century have caused abnormalities in the general circulation of the atmosphere. These abnormalities lead to changes in severity of climate phenomenon's in different parts of the globe. This study aimed to simulate the maximum daily rainfall in Saravan using Artificial Neural Network (ANN). To do this, maximum 24-h rainfall of different months was obtained from synoptic station of Saravan and data of climate indicators from 1986 to 2010 obtained from NOAA website. The effective climate indicators were identified using stepwise method. The data were normalized in the range of 0.1 to 0.9 and the data were applied with 80 to 20 combinations for training data and simulation to neural network model. The used networks were back propagation and Radial Basis with Levenberg-Marquardt training algorithm which created by different combinations of inputs, number of hidden layers and the number of neurons. After creation of mass models; it was found that the chosen network model, Radial Basis, has a better function. This model, with 2 hidden layers of 12 neurons, 0.9578 determination coefficients and less error, presented more acceptable performance in the prediction stage. Comparing the results of chosen ANN and regression models showed that ANN model can accurately predict the daily maximum precipitation. It was found, that the monthly precipitation, maximum and minimum monthly relative humidity, tropical pattern of the South Atlantic Index with 7 months delay and nino1+2 Index with 10 months delay play the main role in daily maximum precipitation in Saravan.

Maximum Daily Rainfall Simulation by using Artificial Neural Network (Case Study: Saravan-Iran)