Long Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods
The monthly precipitation data from 29 stations in Serbia during the period of 1946 2012 were considered. Precipitation trends were calculated using linear regression method. Three CLINO periods (1961 1990, 1971 2000, and 1981 2010) in three subregions were analysed. The CLINO 1981 2010 period had a significant increasing trend. Spatial pattern of the precipitation concentration index (PCI) was presented. For the purpose of PCI prediction, three Support Vector Machine (SVM) models, namely, SVM coupled with the discrete wavelet transform (SVM Wavelet) the firefly algorithm (SVM FFA) and using the radial basis function (SVM RBF) were developed and used. The estimation and prediction results of these models were compared with each other using three statistical indicators, that is, root mean square error, coefficient of determination, and coefficient of efficiency. The experimental results showed that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM Wavelet approach. Moreover, the results indicated the proposed SVM Wavelet model can adequately predict the PCI.