Article(id=1223278136880660899, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20230042, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1673193600000, receivedDateStr=2023-01-09, revisedDate=1675785600000, revisedDateStr=2023-02-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1769581835240, onlineDateStr=2026-01-28, pubDate=1698163200000, pubDateStr=2023-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769581835240, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769581835240, creator=13701087609, updateTime=1769581835240, updator=13701087609, issue=Issue{id=1223278131956551924, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='10', pageStart='1', pageEnd='228', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769581834067, creator=13701087609, updateTime=1769584342407, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223288652869030422, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223288652869030423, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=23, endPage=26, ext={EN=ArticleExt(id=1223278138147340826, articleId=1223278136880660899, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Runoff Prediction and Comparative Study by Combining ICEEMDAN and Multiple Intelligent Optimization Algorithms, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=
In order to improve the accuracy and reliability of runoff prediction, the advantages of EMD in dealing with non-stationary time series are introduced, and a BP neural network prediction model based improved adaptive noise complete set empirical mode decomposition (ICEEMDAN) and whale algorithm (WOA) optimization is established. Taking the inflow runoff prediction of Jinpen Reservoir in Heihe, Shaanxi Province as an example, a simulation model based on multiple intelligent optimization algorithms is established to predict the inflow runoff of the reservoir. At the same time, historical data of different time series, such as precipitation and runoff, are selected as input factors to compare the prediction ability and results of BP, WOA-BP, ICEEMDAN-BP and ICEEMDAN-WOA-BP models under the same input factor conditions. The results show that as far as the input sequence is concerned, the prediction effect of the model with precipitation as the input factor is better than that of the model with runoff as the input factor; For different algorithms, ICEEMDAN-WOA-BP model has good stability, Nash coefficient can reach 80%-90%, and the prediction accuracy is higher. The proposed ICEEMDAN-WOA-BP model can provide technical support for river runoff prediction, reservoir hydrological prediction and watershed water resources management.
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为提高径流预测的精度及可靠性，引入EMD处理非稳态时间序列的优势，建立改进自适应噪声完整集合经验模态分解（ICEEMDAN）和鲸鱼算法（WOA）优化的BP神经网络预测模型。以陕西省黑河金盆水库入库径流预测为例，建立基于多种智能优化算法的模拟模型对水库入库径流进行预测，同时分别选用降水、径流等不同时间序列历史资料作为输入因子，对比在相同输入因子条件下BP、WOA-BP、ICEEMDAN-BP、ICEEMDAN-WOA-BP模型的预测能力。结果表明，就输入序列而言，以降水为输入因子的模型预测效果优于以径流为输入因子的模型；就不同算法而言，ICEEMDAN-WOA-BP模型具有较好的稳定性，纳什系数可达80%~90%，预测精度更高。说明所提出的ICEEMDAN-WOA-BP模型可为河川径流预测、水库水文预报和流域水资源管理等提供技术支撑。
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a.长安大学水利与环境学院，陕西 西安 710054
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毛钦男（1998-），男，硕士研究生，研究方向为水文学及水资源，E-mail：18758860500@163.com
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毛钦男（1998-），男，硕士研究生，研究方向为水文学及水资源，E-mail：18758860500@163.com
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a.长安大学水利与环境学院，陕西 西安 710054
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a.School of Water and Environment, Chang’an University, Xi’an 710054, China
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