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Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
Scientific and accurate prediction of river runoff is important for river flood control and sustainable use of water resources. This study evaluates the ability of a Nonlinear Auto Regressive model (NAR) in predicting runoff volume. Using the Cuntan Hydrological Station in the upper reaches of the Yangtze River as the research object, the model was established based on the runoff characteristics from 1951 to 2020 and tested by NAR. To improve the prediction efficiency, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) preprocessing technique is used to decompose the data. The results show that the coupled CEEMDAN-NAR model has better predictive ability than the single model, with a coupled model deterministic coefficient (DC) of 0.93 and a prediction accuracy of Class A. HIGHLIGHTS Proposed a runoff prediction model using CEEMDAN-NAR hybrid approach.; Decomposition of runoff data using CEEMDAN preprocessing techniques to improve model prediction accuracy.; Using models to predict runoff in the upper Yangtze River from 2005 to 2020 and verifying their accuracy; The CEEMDAN-NAR model provides better predictions than the GRU, NAR and EEMD-NAR prediction models.;
Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
Scientific and accurate prediction of river runoff is important for river flood control and sustainable use of water resources. This study evaluates the ability of a Nonlinear Auto Regressive model (NAR) in predicting runoff volume. Using the Cuntan Hydrological Station in the upper reaches of the Yangtze River as the research object, the model was established based on the runoff characteristics from 1951 to 2020 and tested by NAR. To improve the prediction efficiency, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) preprocessing technique is used to decompose the data. The results show that the coupled CEEMDAN-NAR model has better predictive ability than the single model, with a coupled model deterministic coefficient (DC) of 0.93 and a prediction accuracy of Class A. HIGHLIGHTS Proposed a runoff prediction model using CEEMDAN-NAR hybrid approach.; Decomposition of runoff data using CEEMDAN preprocessing techniques to improve model prediction accuracy.; Using models to predict runoff in the upper Yangtze River from 2005 to 2020 and verifying their accuracy; The CEEMDAN-NAR model provides better predictions than the GRU, NAR and EEMD-NAR prediction models.;
Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
Xianqi Zhang (author) / Zhiwen Zheng (author) / Kai Wang (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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