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[1]HU Zuolong,GAO Peng.Runoff Prediction in the Upper Reaches of Beiluo River Based on EEMD-SVM Model[J].Research of Soil and Water Conservation,2023,30(04):98-102,109.[doi:10.13869/j.cnki.rswc.2023.04.021.]

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Runoff Prediction in the Upper Reaches of Beiluo River Based on EEMD-SVM Model

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 30 Number of periods: 2023 04 Page number: 98-102,109 Column: Public date: 2023-06-10

Title:: Runoff Prediction in the Upper Reaches of Beiluo River Based on EEMD-SVM Model

Author(s):: HU Zuolong¹, GAO Peng²; (1.Henan Water Environment Survey and Design Co., Ltd., Sanmenxia, Henan 472000, China; 2.State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi 712100, China)

Keywords:: runoff prediction; ensemble empirical mode decomposition; support vector machine; Beiluo River

CLC:: P338

DOI:: 10.13869/j.cnki.rswc.2023.04.021.

Abstract:: [Objective] The aim of this study is to improve the accuracy of runoff forecast in the upper reaches of Beiluo River and provide basis for watershed management and rational allocation of water resources. [Methods] Based on the measured runoff data of Wuqi hydrological station in the upper reaches of Beiluo River from 1971 to 2014, the monthly runoff series was predicted by using EEMD-SVM coupling model, and compared with the prediction results of EEMD-ARIMA and EEMD-NAR models. [Results] EEMD-SVM model has the lowest mean absolute error and root mean square error, and the highest coefficient of determination(R²)and Nash coefficient. Compared with EEMD-ARIMA and EEMD-NAR models, the R² value of EEMD-SVM model increases by 186.63% and 49.49%, respectively. [Conclusion] EEMD-SVM model has higher prediction accuracy and stronger nonlinear fitting ability, and can be successfully applied to the monthly runoff prediction in the upper reaches of Beiluo River. At the same time, the prediction performance of EEMD-NAR model is higher than that of EEMD-ARIMA model.

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Last Update: 2023-06-10