A Novel Nonlinear Combination Model Based on Support Vector Machine for Rainfall Prediction

In this study, a novel modular-type Support Vector Machine (SVM) is presented to simulate rainfall prediction. First of all, a bagging sampling technique is used to generate different training sets. Secondly, different kernel function of SVM with different parameters, i.e., base models, are then trained to formulate different regression based on the different training sets. Thirdly, the Partial Least Square (PLS) technology is used to select choose the appropriate number of SVR combination members. Finally, a $\nu$-SVM can be produced by learning from all base models. The technique will be implemented to forecast monthly rainfall in the Guangxi, China. Empirical results show that the prediction by using the SVM combination model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. Our findings reveal that the nonlinear ensemble model proposed here can be used as an alternative forecasting tool for a Meteorological application in achieving greater forecasting accuracy and improving prediction quality further.