Spatial rainfall prediction using optimal features selection approaches

Rainfall as a semi-random hydrological event is difficult to forecast due to some very complicated and unforeseen physical factors and their chaotic behavior. Artificial neural networks (ANN), which perform a nonlinear mapping between inputs and outputs, have played a crucial role in rainfall forecasting. In this paper, some feature selection approaches have been implemented to simulate the regional scale rainfall field in order to address a few deficiencies of ANN, such as selection of informative features of input data encountered in hydrological processes. The main simulator is a multi-layer perceptron neural network optimized by simple genetic algorithm (GA) to determine optimal input vectors in order to compare with other statistical approaches. Current rainfall from a limited number of neighboring stations is shown to be valuable to forecast current rainfall of certain target stations in the province of Fars in Iran for 30 min leading time. Among the studied features selection approaches such as chi-squared, linear correlation coefficient and mutual information (MI), the results by MI have considerable competency with regard to computational efficiency using the optimized scenario by GA.