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 Y...

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Autores principales:	Xianqi Zhang, Zhiwen Zheng, Kai Wang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ceemdan nar runoff projections upper yangtze river Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/e2e29b37b14b404abc7e21c1e4595153
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id	oai:doaj.org-article:e2e29b37b14b404abc7e21c1e4595153
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spelling	oai:doaj.org-article:e2e29b37b14b404abc7e21c1e45951532021-11-23T18:55:27ZPrediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model1606-97491607-079810.2166/ws.2021.121https://doaj.org/article/e2e29b37b14b404abc7e21c1e45951532021-11-01T00:00:00Zhttp://ws.iwaponline.com/content/21/7/3307https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Scientific 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.;Xianqi ZhangZhiwen ZhengKai WangIWA Publishingarticleceemdannarrunoff projectionsupper yangtze riverWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 7, Pp 3307-3318 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ceemdan nar runoff projections upper yangtze river Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	ceemdan nar runoff projections upper yangtze river Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Xianqi Zhang Zhiwen Zheng Kai Wang Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
description	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.;
format	article
author	Xianqi Zhang Zhiwen Zheng Kai Wang
author_facet	Xianqi Zhang Zhiwen Zheng Kai Wang
author_sort	Xianqi Zhang
title	Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
title_short	Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
title_full	Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
title_fullStr	Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
title_full_unstemmed	Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model
title_sort	prediction of runoff in the upper yangtze river based on ceemdan-nar model
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/e2e29b37b14b404abc7e21c1e4595153
work_keys_str_mv	AT xianqizhang predictionofrunoffintheupperyangtzeriverbasedonceemdannarmodel AT zhiwenzheng predictionofrunoffintheupperyangtzeriverbasedonceemdannarmodel AT kaiwang predictionofrunoffintheupperyangtzeriverbasedonceemdannarmodel
_version_	1718416186087047168

Prediction of runoff in the upper Yangtze River based on CEEMDAN-NAR model

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