Precipitation Prediction Based on KPCA Support Vector Machine Optimization

Abstract

In this paper, kernel principle component analysis (KPCA) is employed to extract the features of multiple precipitation factors. The extracted principle components are considered as the characteristic vector of support vector machine (SVM) to build the SVM precipitation forecast model. We calculate the SVM parameters using particle swarm optimization (PSO) algorithm, and build the cooperative model of KPCA and the SVM with PSO to predict the precipitation in Guangxi province. The simulation results show that the prediction outcome, resulting from the combination of KPCA and the SVM with PSO, is consistent with the actual precipitation. Comparisons with other models also demonstrate that our model has advantages in fitting and generalizing in comparison other models.