Article(id=1223204291607904285, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221887, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1662825600000, receivedDateStr=2022-09-11, revisedDate=1668700800000, revisedDateStr=2022-11-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1769564229155, onlineDateStr=2026-01-28, pubDate=1684944000000, pubDateStr=2023-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769564229155, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769564229155, creator=13701087609, updateTime=1769564229155, updator=13701087609, issue=Issue{id=1223204286050452333, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='5', 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=1769564227831, creator=13701087609, updateTime=1769567742010, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223219026013323264, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223219026013323265, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223204286050452333, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=71, endPage=75, ext={EN=ArticleExt(id=1223204292757143625, articleId=1223204291607904285, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Dam Deformation Prediction Model and Its Application Based on FCM-WOA-LSTM, columnId=1222925280734335368, journalTitle=Water Resources and Power, columnName=DAM SAFETY AND MONITORING, runingTitle=null, highlight=null, articleAbstract=

With the continuous accumulation of dam deformation monitoring data and the continuous increase of deformation measuring points, it often takes a lot of time to predict all deformation measuring points, which is easy to cause the problem of untimely feedback. Therefore, the fuzzy C-means clustering algorithm (FCM) was introduced to partition the dam according to the similarity of deformation laws. The whale optimization algorithm (WOA) was used to optimize the parameters of long short-term memory neural network (LSTM), and a dam deformation prediction model based on FCM-WOA-LSTM was established. The measured deformation data of a concrete double-curvature arch dam was used as sample data for prediction, and the prediction results were compared with those of LSTM model and SVM model. The results show that the average absolute error (M_MAE), mean square error (M_MSE) and root mean square error (R_RMSE) of the prediction results of FCM-WOA-LSTM model are the smallest among the three models, and the three evaluation indexes of the fitting section are close to those of the prediction section, respectively. Compared with the existing models, the FCM-WOA-LSTM model has higher prediction accuracy and better applicability.

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随着大坝变形监测资料的持续积累和变形测点数量的不断增多，预测分析全部变形测点往往需耗费大量的时间，容易造成反馈不及时的问题。对此，引入模糊C-均值聚类算法（FCM），根据大坝变形规律的相似程度进行分区，将鲸鱼优化算法（WOA）用于长短期记忆神经网络（LSTM）模型的参数优化，建立基于FCM-WOA-LSTM的大坝变形预测模型，以某混凝土双曲拱坝的实测变形资料作为样本数据进行预测分析，并与LSTM模型和SVM模型的预测结果进行对比。结果表明，FCM-WOA-LSTM模型预测结果的平均绝对误差M_MAE、均方误差M_MSE、均方根误差R_RMSE均为3种模型中最小，且拟合段的3个评价指标值和预测段的3个评价指标值均接近，FCM-WOA-LSTM模型具有更高的预测精度和更好的适用性。

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^b.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1223204294749438141, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223204291607904285, authorId=1223204294548111527, language=CN, stringName=曹梦茜, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{a, b}, address=^a.河海大学水利水电学院，江苏 南京 210098
^b.河海大学水文水资源与水利工程科学国家重点实验室，江苏 南京 210098, bio={"content":"

曹梦茜（1998-），女，硕士研究生，研究方向为水工结构安全监测，E-mail：690817850@qq.com

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曹梦茜（1998-），女，硕士研究生，研究方向为水工结构安全监测，E-mail：690817850@qq.com

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