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Abstract
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This study compared two machine learning techniques, support vector machines (SVM), and artificial neural network (ANN) in modeling monthly precipitation fluctuations. The SVM and ANN approaches were applied to the monthly precipitation data of two synoptic stations in Hamadan (Airport and Nojeh), the west of Iran. To avoid overfitting, the data were divided into two parts of training (70 %) and test sets (30 %). Then, monthly data from July 1976 to June 2001 and data from April 1961 to November 1996 were considered as training set for the Hamadan and Nojeh stations, respectively, and the remaining were used as test set. The results of the SVM model were compared with those of the ANN based on the root mean square errors, mean absolute errors, determination coefficient, and efficiency coefficient criteria. Based on the comparison, it was found that the SVM model outperformed the ANN, and the estimated precipitation values were in good agreement with the corresponding observed values.
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