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A new flood forecasting model based on SVM and boosting learning algorithms

Support vector machine (SVM) has been widely applied in flood forecasting models and achieved good results. However, it has been plagued by two problems. One is its over-reliance on the number and quality of the raw input data; the other is that a single model cannot describe the complex relationships hidden in the flood evolution processes. To tackle the two problems, this paper presents an SVM flood forecasting model based on kernel principal component analysis(KPCA) and boosting algorithm. The nonlinear KPCA is applied to extract the useful information from historical flood data. To eliminate the interference caused by redundant information, boosting learning algorithm with multiple SVM models exploits various distribution characteristics of the historical flood data. Each sub-model focuses on the learning of a certain type of samples. Finally the prediction is achieved through combining multiple models. Application to the flood forecasting of Wangjiaba station at Huaihe River shows that the proposed SVM ensemble model based on KPCA and boosting learning can improve the flood forecasting accuracy effectively.