A novel medium- and long-term runoff combined forecasting model based on different lag periods. Issue 2 (21st February 2022)

• Record Type: Journal Article
• Authors: Ai, Ping; Song, Yanhong; Xiong, Chuansheng; Chen, Binbin; Yue, Zhaoxin
• Is Part Of: Journal of hydroinformatics. Volume 24:Issue 2(2022)
• Page Start: 367
• Page End: 387
• Publication Date: 2022-02-21
• Subjects: combined model -- delay correlation analysis -- extreme learning machine -- medium- and long-term runoff forecasting -- optimization algorithm; Hydrology -- Data processing -- Periodicals; Geographic information systems -- Periodicals; Geographic information systems; Hydrology -- Data processing; Electronic journals; Periodicals; 551.480285
• DOI: 10.2166/hydro.2022.116
• Languages: English
• ISSNs: 1464-7141

Abstract

Abstract: 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 with different lag periods was used to select the key factors. A novel medium- and long-term runoff combined forecasting model based on different lag periods was proposed, which performed better than other models. The lag period of physical factors delay can affect the accuracy of runoff forecasting. Graphical Abstract