Article(id=1311351266131932157, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2026.20250488, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1742140800000, receivedDateStr=2025-03-17, revisedDate=1744041600000, revisedDateStr=2025-04-08, acceptedDate=1744214400000, acceptedDateStr=2025-04-10, onlineDate=1790580105573, onlineDateStr=2026-09-28, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790580105573, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790580105573, creator=13701087609, updateTime=1790580105573, updator=13701087609, issue=Issue{id=1311351152067830064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2026', volume='44', issue='1', pageStart='1', pageEnd='246', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790580078378, creator='13701087609', updateTime=1790588459421, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311386304772927717, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311386304772927718, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=182, endPage=187, ext={EN=ArticleExt(id=1311351266308092926, articleId=1311351266131932157, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Research on Hybrid Identification Algorithm for Anomaly Patterns in Gravity Dam Monitoring Data Based on VMD-SSA-K-means-iForest, columnId=null, journalTitle=Water Resources and Power, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Anomaly detection in gravity dam monitoring data is critical for dam safety assessment. To overcome limitations of existing methods in pattern identification and feature extraction, this paper proposes a hybrid anomaly pattern detection method based on VMD-SSA-KMeans-iForest. By introducing variational mode decomposition (VMD) to optimize the decomposition process, the accuracy and robustness of feature extraction is significantly improved. Furthermore, an anomaly detection framework is established by combining K-means with isolation forest. This method is applied to the identification of abnormal data of the W gravity dam. The results demonstrate that the proposed method achieves a 2.5% improvement in anomaly detection accuracy while effectively distinguishing between anomalies caused by structural damage and those resulting from instrument failure,which provides more reliable technical support for the safety assessment of gravity dams.

, authors=Tie LI¹, Han-man LI^{2a, 2b}, Fu-sheng WANG¹, Liang XU¹, Rui GUO¹, authorsList=Tie LI, Han-man LI, Fu-sheng WANG, Liang XU, Rui GUO, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311351267272782847, articleId=1311351266131932157, tenantId=1146029695717560320, journalId=1205116964453384197, language=CN, title=基于VMD-SSA-K-means-iForest的重力坝监测数据异常模式混合识别算法研究, columnId=1207271181661589640, journalTitle=水电能源科学, columnName=水利枢纽、水利建筑物, runingTitle=null, highlight=null, articleAbstract=

重力坝监测数据的异常识别对大坝安全评估具有重要意义，针对现有方法在模式辨识和特征提取方面的局限性，提出一种基于VMD-SSA-KMeans-iForest的重力坝监测数据异常值混合识别方法,该方法通过引入变分模态分解（VMD）优化SSA分解过程，显著提升了特征提取的精度和鲁棒性。在此基础上，构建了基于K-means聚类与孤立森林（iForest）协同的异常识别框架，并将该方法应用于W重力坝异常数据识别中。结果表明，所提方法的异常识别准确率提升了2.5%，同时有效区分了结构损伤与仪器故障引起的异常模式，为重力坝安全评估提供了更可靠的技术支持。

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李铁（1978-），男，高级工程师，研究方向为水利规划设计，E-mail: 655901@qq.ocm

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李铁（1978-），男，高级工程师，研究方向为水利规划设计，E-mail: 655901@qq.ocm

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