Affiliation:
1. College of Computer and Information Engineering, Hohai University, Nanjing 211100, China
2. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
3. College of Computer and Software, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
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.
Funder
Major Research plan of the National Natural Science Foundation of China
the school research fund of Nanjing Vocational University of Industry Technology
Subject
Atmospheric Science,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering,Water Science and Technology
Reference34 articles.
1. Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model;Natural Hazards,2021
2. A novel combined model based on echo state network for multi-step ahead wind speed forecasting: a case study of NREL;Energy Conversion and Management,2019
3. Runoff variation characteristics, association with large-scale circulation and dominant causes in the Heihe River Basin, Northwest China;Science of the Total Environment,2019
4. Improved medium- and long-term runoff forecasting using a multimodel approach in the Yellow River headwaters region based on large-scale and local-scale climate information;Water,2017
5. Modeling the influence of meteorological variables on runoff in a tropical watershed;Civil Engineering Journal,2020
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