Article(id=1223190875400950316, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20230577, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1678550400000, receivedDateStr=2023-03-12, revisedDate=1682611200000, revisedDateStr=2023-04-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561030483, onlineDateStr=2026-01-28, pubDate=1695571200000, pubDateStr=2023-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561030483, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561030483, creator=13701087609, updateTime=1769561030483, updator=13701087609, issue=Issue{id=1223190866320278179, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='9', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769561028318, creator=13701087609, updateTime=1769562015483, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223195006870082478, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223195006870082479, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=19, endPage=22, ext={EN=ArticleExt(id=1223190875765854793, articleId=1223190875400950316, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Maximum Erosion Depth Prediction of River Bend Based on IF-GEP, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

In order to address the limitations in forecasting the maximum scour depth of conventional river bends, this study amalgamated the methodologies of isolated forest (IF) and gene expression programming (GEP). An IF-GEP model for predicting the maximum scour depth of river bends was established. The validation results demonstrate that the IFGEP prediction model surpasses existing formulations in terms of its accuracy on the test set. Moreover, it exhibits enhanced predictive performance compared to the traditional GS-SVR and RF models. Application of the prediction model to various rivers yielded remarkably close results to the actual measured values, affirming its strong predictive capability and robust generalization performance.

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为解决传统河流流经弯道的最大冲刷深度预测过程中存在的不足，将孤立森林（IF）和基因表达式编程（GEP）方法相结合，建立了一个基于IF的GEP河湾最大冲刷深度预测模型（IF-GEP），并将该模型与传统GS-SVR和RF模型及现有经验公式进行对比。结果表明，IF-GEP预测模型在测试集上取得了较好的预测效果，且预测精度明显高于现有公式及传统的GS-SVR和RF模型。最后将该预测模型应用于多条不同河流的预测中，IF-GEP预测模型的预测结果与实际测量值较吻合，说明该预测模型具有良好的预测能力和较高的泛化性能。

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陈骏峰（1999-），男，硕士研究生，研究方向为水力学及河流动力学，E-mail：1044128575@qq.com

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陈骏峰（1999-），男，硕士研究生，研究方向为水力学及河流动力学，E-mail：1044128575@qq.com

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