Monthly runoff prediction using modified CEEMD-based weighted integrated model

Due to the nonlinear characteristics of runoff data and the poor performance of the single prediction model, a weighted integrated modified complementary ensemble empirical mode decomposition (MCEEMD)-based model was proposed to predict the monthly runoff of three hydrological stations in the lower...

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Autores principales:	Xinqing Yan, Yuan Chang, Yang Yang, Xuemei Liu
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	integrated model modified complementary ensemble empirical mode decomposition monthly runoff prediction particle swarm optimization weight coefficient Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/374dc0777e9e4b929db6418ee9a1faca
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id	oai:doaj.org-article:374dc0777e9e4b929db6418ee9a1faca
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spelling	oai:doaj.org-article:374dc0777e9e4b929db6418ee9a1faca2021-11-05T19:01:52ZMonthly runoff prediction using modified CEEMD-based weighted integrated model2040-22442408-935410.2166/wcc.2020.274https://doaj.org/article/374dc0777e9e4b929db6418ee9a1faca2021-08-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/5/1744https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354Due to the nonlinear characteristics of runoff data and the poor performance of the single prediction model, a weighted integrated modified complementary ensemble empirical mode decomposition (MCEEMD)-based model was proposed to predict the monthly runoff of three hydrological stations in the lower reaches of the Yellow River. In this model, particle swarm optimization (PSO) was used to optimize the parameters of support vector regression (SVR), back propagation neural network (BP), long short-term memory neural network (LSTM) that constitute it. The weight coefficients and frequency terms decomposed by MCEEMD were used to obtain the final prediction results. Results indicated that this model performs better than other models, with the Nash–Sutcliffe efficiency (NSE) reaching above 0.92, qualification rate (QR) reaching above 75% and all error indicators being minimal. In addition, considering the influence of extreme weather and climate anomalies, the integrated model combined the atmospheric circulation anomalies factors and the results can still be improved. It can be verified that this weighted integrated model can be used for the stable and accurate predication of medium- and long-term runoff. HIGHLIGHTS Decomposition of runoff into smooth sequences using MCEEMD reduces the accumulation of errors associated with the CEEMD.; A weighted integrated method was used to develop predictive models based on the MCEEMD decomposition.; In view of the influence of extreme weather and abnormal climate on runoff prediction accuracy, the atmospheric circulation anomaly factors were used as the input data of the model to predict.;Xinqing YanYuan ChangYang YangXuemei LiuIWA Publishingarticleintegrated modelmodified complementary ensemble empirical mode decompositionmonthly runoff predictionparticle swarm optimizationweight coefficientEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 5, Pp 1744-1760 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	integrated model modified complementary ensemble empirical mode decomposition monthly runoff prediction particle swarm optimization weight coefficient Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	integrated model modified complementary ensemble empirical mode decomposition monthly runoff prediction particle swarm optimization weight coefficient Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Xinqing Yan Yuan Chang Yang Yang Xuemei Liu Monthly runoff prediction using modified CEEMD-based weighted integrated model
description	Due to the nonlinear characteristics of runoff data and the poor performance of the single prediction model, a weighted integrated modified complementary ensemble empirical mode decomposition (MCEEMD)-based model was proposed to predict the monthly runoff of three hydrological stations in the lower reaches of the Yellow River. In this model, particle swarm optimization (PSO) was used to optimize the parameters of support vector regression (SVR), back propagation neural network (BP), long short-term memory neural network (LSTM) that constitute it. The weight coefficients and frequency terms decomposed by MCEEMD were used to obtain the final prediction results. Results indicated that this model performs better than other models, with the Nash–Sutcliffe efficiency (NSE) reaching above 0.92, qualification rate (QR) reaching above 75% and all error indicators being minimal. In addition, considering the influence of extreme weather and climate anomalies, the integrated model combined the atmospheric circulation anomalies factors and the results can still be improved. It can be verified that this weighted integrated model can be used for the stable and accurate predication of medium- and long-term runoff. HIGHLIGHTS Decomposition of runoff into smooth sequences using MCEEMD reduces the accumulation of errors associated with the CEEMD.; A weighted integrated method was used to develop predictive models based on the MCEEMD decomposition.; In view of the influence of extreme weather and abnormal climate on runoff prediction accuracy, the atmospheric circulation anomaly factors were used as the input data of the model to predict.;
format	article
author	Xinqing Yan Yuan Chang Yang Yang Xuemei Liu
author_facet	Xinqing Yan Yuan Chang Yang Yang Xuemei Liu
author_sort	Xinqing Yan
title	Monthly runoff prediction using modified CEEMD-based weighted integrated model
title_short	Monthly runoff prediction using modified CEEMD-based weighted integrated model
title_full	Monthly runoff prediction using modified CEEMD-based weighted integrated model
title_fullStr	Monthly runoff prediction using modified CEEMD-based weighted integrated model
title_full_unstemmed	Monthly runoff prediction using modified CEEMD-based weighted integrated model
title_sort	monthly runoff prediction using modified ceemd-based weighted integrated model
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/374dc0777e9e4b929db6418ee9a1faca
work_keys_str_mv	AT xinqingyan monthlyrunoffpredictionusingmodifiedceemdbasedweightedintegratedmodel AT yuanchang monthlyrunoffpredictionusingmodifiedceemdbasedweightedintegratedmodel AT yangyang monthlyrunoffpredictionusingmodifiedceemdbasedweightedintegratedmodel AT xuemeiliu monthlyrunoffpredictionusingmodifiedceemdbasedweightedintegratedmodel
_version_	1718444030634754048

Monthly runoff prediction using modified CEEMD-based weighted integrated model

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