Establishment and application of hydrological time series forecasting model based on KPCA_SVM.

【Objective】 The KPCA_SVM model of hydrological time series forecasting model was established.【Method】 The method of Kernel Principle Component Analysis(KPCA) was used to obtain the feature information,and then the obtained series was used as input of Least Square Support Vector Machine model for forecasting.With monthly evaporation in the Minqin region as an example,it was applied to test forecasting result of model.【Result】 The results show that the KPCA_SVM model had a better effect on forecasting than PCA_SVM and LSSVM,and the average error was 8.36%.【Conclusion】 The forecasting effect of KPCA_SVM model was much better than that of LSSVM model without obtaining the feature information.In comparison with PCA,the performance of KPCA was better,too.