A novel medium- and long-term runoff combined forecasting model based on different lag periods

The accuracy of medium- and long-term runoff forecasting plays a significant role in several applications involving the management of hydrological resources, such as power generation, water supply and FLood mitigation. Numerous studies that adopted combined forecasting models to enhance runoff forecasting accuracy have been proposed. Nevertheless, some models do not take into account the effects of different lag periods on the selection of input factors. Based on this, this paper proposed a novel medium- and long-term runoff combined forecasting model based on different lag periods. In this approach, the factors are initially selected by the time-delay correlation analysis method of different lag periods and further screened with stepwise regression analysis. Next, an extreme learning machine (ELM) is adopted to integrate each result obtained from the three single models, including multiple linear regression (MLR), feed-forward back propagation-neural network (FFBP-NN) and support vector regression (SVR), which is optimized by particle swarm optimization (PSO). To verify the effectiveness and versatility of the proposed combined model, the Lianghekou and Jinping hydrological stations from the Yalong River basin, China, are utilized as case studies. The experimental results indicate that compared with MLR, FFBP-NN, SVR and ridge regression (RR), the proposed combined model can better improve the accuracy of medium- and long-term runoff forecasting in the statistical indices of MAE, MAPE, RMSE, DC, U95 and reliability. HIGHLIGHTS The delay correlation analysis method with different lag periods was used to select the key factor. A novel combined forecasting method was proposed, which puts the intermediate forecasting results of the individual model into an extreme learning machine. The proposed combined model performs better than the individual model and the traditional combination method. The lag period of physical factor delay does affect the accuracy of runoff prediction.