Integrated forecasting method of medium-and long-term runoff by ridge regression based on optimal sub-model selection

Abstract

Numerous studies have demonstrated that the combination models can improve the runoff forecast performance compared to individual forecasts. However, some models do not take into account the effects of inappropriate sub-models on the combination models. Based on this, a medium-and long-term runoff integrated forecasting method based on optimal sub-models selection was proposed. First, the sub-models, including linear regression (MLR), BP neural network (BPNN), wavelet neural network (WNN), and support vector regression (SVR), are optimally selected based on the nearness degree. Second, ridge regression (RR) is used to combine the optimal sub-models to predict runoff. Finally, the Guandi hydropower station is taken as an example to verify the effect of the integrated forecasting model. The results show that SVR, BPNN, and WNN are the optimal sub-models, and RR-3 is the optimal integrated forecasting model composed of the optimal sub-models. In addition, compared with the other two combination models, the RR-3 performs better. HIGHLIGHTS The nearness degree was proposed to select the optimal sub-models in the medium- and long-term runoff combination forecasting.; A combination prediction method using RR to predict the medium- and long-term runoff is established.; The sub-models can affect the accuracy of runoff combination forecasting.;

Keywords

• integrated forecast
• medium- and long-term runoff
• nearness degree
• ridge regression
• sub-model optimization