Article(id=1282030080478131155, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.08.0155, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1741881600000, receivedDateStr=2025-03-14, revisedDate=1750176000000, revisedDateStr=2025-06-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589390152, onlineDateStr=2026-07-09, pubDate=1756310400000, pubDateStr=2025-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589390152, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589390152, creator=13701087609, updateTime=1783589390152, updator=13701087609, issue=Issue{id=1282030021623657005, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='8', pageStart='1', pageEnd='276', issueExtLink='null', onlineDate='null', pubDate='1756310400000', pubDateStr='2025-08-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589376121, creator='13701087609', updateTime=1783589731222, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031511155228698, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031511155228699, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=164, endPage=170, ext={EN=ArticleExt(id=1282030080692040660, articleId=1282030080478131155, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Dam anomaly detection model based on improved Prophet-LSTM-PSO, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

In order to improve the anomaly detection performance of dam monitoring data, a dam abnormal data detection method based on the improved Prophot-long short term memory-particle swarm optimization Prophet-LSTM-PSO was proposed. Firstly, by improving the Prophet method, the trend component features obtained from the decomposition of abnormal data points were clearly visible. Secondly, the decomposed trend, periodic, and residual components were represented in a three-dimensional space, where the original time series data was substituted with the mean distance of the nearest neighbors in this space. Finally, abnormal data points were identified precisely by combining the LSTM network and PSO algorithm to set and optimize anomaly thresholds. The results show that the method proposed in this paper significantly improves detection performance and exhibits high stability compared with traditional methods. Notably, while maintaining a stable recall rate exceeding 95%, both accuracy and precision surpass 95%, thereby validating the effectiveness and practicality of the proposed method.

, authors=Dalong GE¹, Yong DING^{**, 1}, Denghua LI^{2, 3}, authorsList=Dalong GE, Yong DING, Denghua LI, authorCompany=null, correspAuthors=Yong DING, 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=1282030081145025495, articleId=1282030080478131155, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于改进Prophet-LSTM-PSO的大坝异常数据检测模型, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为提升大坝监测数据的异常检测性能，提出一种基于改进Prophet-长短期记忆（LSTM）-粒子群优化（PSO）的大坝异常数据检测模型。首先，通过改进Prophet法使得异常数据点位分解得到趋势分量特征；其次，将分解得到的趋势、周期和残差分量映射到三维空间，以三维空间中近邻均值距离数据代替原始时序数据；最后，结合LSTM网络与PSO算法，设定与优化异常阈值，进而实现异常数据的精准识别。结果表明：相较于传统模型，该模型在检测效果上具有明显提升，且表现出较高的稳定性。在召回率稳定维持在95%以上的前提下，精确率与准确率均超过95%，验证了该方法的有效性与实用性。

, authors=葛大龙¹, 丁勇副教授^{**, 1}, 李登华高级工程师^{2, 3}, authorsList=葛大龙, 丁勇副教授, 李登华高级工程师, authorCompany=null, correspAuthors=丁勇副教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=vWJ2RCCyEaGeSND9wtoNLA==, magXml=LCS1xBih0X2/BAZYPZP0qQ==, pdfUrl=null, pdf=bSuF6O9GLx7gRGU/0s/LcA==, pdfFileSize=4689732, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=eTfo64Zyqkmf8mNe5n9bPw==, mapNumber=null, fund=null)}, authors=[Author(id=1282030081656730594, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030080478131155, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=cr1026916@foxmail.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282030081719645156, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030080478131155, authorId=1282030081656730594, language=EN, stringName=Dalong GE, firstName=Dalong, middleName=null, lastName=GE, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Safety Science and Engineering, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282030081795142629, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030080478131155, authorId=1282030081656730594, language=CN, stringName=葛大龙, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹南京理工大学 安全科学与工程学院，江苏 南京 210094, bio={"content":"

葛大龙（2000—）男，河南信阳人，硕士研究生，主要研究方向为大坝结构健康监测。E-mail: cr1026916@foxmail.com。

"}, bioImg=null, bioContent=

葛大龙（2000—）男，河南信阳人，硕士研究生，主要研究方向为大坝结构健康监测。E-mail: cr1026916@foxmail.com。

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