Combined model of chaos theory, wavelet and support vector machine for forecasting runoff series and its application

A combined model of chaos theory, wavelet and support vector machine was built to overcome the limitations including challenges in determination of orders of nonlinear models and low prediction accuracy which the simulated accuracy is high in runoff series forecasting. Firstly, runoff series were decomposed into different frequency runoff components in application of wavelet. Secondly, phase space was reconstructed in chaotic analysis. Thirdly, support vector machine (SVM) was used to predict each component. Finally, all components were combined into a model to predict runoff. A case study, annual and monthly runoff of two reservoirs located in the Sha River and Li River of the Shaying River system within the Haihe River watershed were used to examine the combined model. The results indicated that the simulated accuracy and predicted accuracy were grade A and grade B, which met the requirements of the medium term accuracy and long term accuracy and the combined model is applicable to medium term and long term prediction.