Article(id=1223190877368079022, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20230299, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1677686400000, receivedDateStr=2023-03-02, revisedDate=1681401600000, revisedDateStr=2023-04-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561030952, onlineDateStr=2026-01-28, pubDate=1695571200000, pubDateStr=2023-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561030952, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561030952, creator=13701087609, updateTime=1769561030952, 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=202, endPage=206, ext={EN=ArticleExt(id=1223190879012246230, articleId=1223190877368079022, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Discharge Prediction Model of Cylindrical Weir-Gate Based on Intelligent Algorithm, columnId=1222925284869922957, journalTitle=Water Resources and Power, columnName=ELECTROMECHANICS AND CONTROL ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

The weir-gate structure has larger discharge capacity. To accurately and efficiently check the discharge of weir-gate, three intelligent algorithms including BP neural network, SVM and GRNN were used to predict the discharge coefficient of cylindrical weir-gate. The correlation analysis and variation law between dimensionless parameters and discharge coefficient were discussed. The results show that the GRNN and the BP can accurately predict the discharge coefficient of the cylindrical weir-gate. The determination coefficient of the BP in the test stage is 0.997, the root mean square error is 0.009, the average absolute percentage is 0.801 %, and the Nash efficiency coefficient is 0.997, which is superior to the GRNN, and it can be used as an efficient and high-precision prediction model for the discharge coefficient of the weir-gate. There is a stronger correlation between the ratio of gate opening to cylinder diameter (a/D), the ratio of weir head to cylinder diameter (H_w/D) and C_d. The C_d increased with the increase of upstream Froude number (Fr) and H_w/D, and the greater the a/D is, the greater the increase of C_d is. The search results provide theoretical reference and technical support for the popularization and application of cylindrical weir gate in practical engineering.

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堰闸结构具有较大的泄流能力，为准确高效校核堰闸流量，采用BP神经网络、支持向量机（SVM）和广义回归神经网络（GRNN）3种智能算法对圆柱形堰闸流量系数（C_d）进行预测，并探究了无量纲参数与流量系数之间的相关性及变化规律。结果表明，广义回归神经网络GRNN模型和BP神经网络模型均可准确预测圆柱形堰闸流量系数，BP模型测试阶段决定系数为0.997，均方根误差为0.009，平均绝对百分比为0.801%，纳什效率系数为0.997，优于GRNN模型，可作为堰闸流量系数的高效高精度预测模型。闸门开度与圆柱直径之比（a/D）、堰顶水头与圆柱直径之比（H_w/D）与C_d之间具有强相关性。C_d随上游弗劳德数（Fr）、H_w/D的增加而增加，a/D越大，C_d增幅越大。研究结果可为圆柱形堰闸在实际工程中的推广应用提供理论参考和技术支撑。

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曹顶业（2000-），女，硕士研究生，研究方向为水力学及河流动力学，E-mail：978431515@qq.com

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曹顶业（2000-），女，硕士研究生，研究方向为水力学及河流动力学，E-mail：978431515@qq.com

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