Abstract
Precipitation prediction is of dispensable importance in many hydrological applications. In this study, monthly precipitation data sets from Serbia for the period 1946-2012 were used to estimate precipitation. To fulfil this objective, three mathematical techniques named artificial neural network (ANN), genetic programming (GP) and support vector machine with wavelet transform algorithm (WT-SVM) were applied. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R-2) were used to evaluate the performance of the WT-SVM, GP and ANN models. The achieved results demonstrate that the WT-SVM outperforms the GP and ANN models for estimating monthly precipitation.