Article(id=1282037405054120818, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.12.0023, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1754409600000, receivedDateStr=2025-08-06, revisedDate=1760025600000, revisedDateStr=2025-10-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1783591136467, onlineDateStr=2026-07-09, pubDate=1766851200000, pubDateStr=2025-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783591136467, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783591136467, creator=13701087609, updateTime=1783591136467, updator=13701087609, issue=Issue{id=1282037390130790473, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='12', pageStart='1', pageEnd='259', issueExtLink='null', onlineDate='null', pubDate='1766851200000', pubDateStr='2025-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783591132910, creator='13701087609', updateTime=1783593783109, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048505929777979, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048505929777980, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=70, endPage=77, ext={EN=ArticleExt(id=1282037405272224627, articleId=1282037405054120818, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Real-time prediction model for landslide displacement based on PSO-GRU-OL-MRT, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To address the challenges that static prediction models face in accurately forecasting dynamic landslide trends and the high computational costs associated with dynamic models—which hinder real-time prediction—this study proposes a novel real-time model for landslide displacement prediction. The model integrated Particle Swarm Optimization (PSO), a Gated Recurrent Unit (GRU) network, Online Learning (OL), and dynamic Model Retraining (MRT). First, a static landslide prediction model was established by integrating PSO and GRU. Second, an OL strategy was incorporated into the static model, enabling dynamic updates and real-time predictions as new monitoring data were acquired. Then, small-batch MRT was performed based on prediction accuracy evaluation to predict landslide trends dynamically and in real time. Finally, a comparative analysis of several related models was conducted using the Wangeryan landslide in Sichuan Province as a case study. The results indicate that the OL and MRT methods significantly improve prediction accuracy. Specifically, the PSO-GRU-OL-MRT model achieved Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R² values of 0.795, 3.53, 1.40, and 0.954, respectively, with an average prediction time of 25.0 seconds per instance, demonstrating the highest prediction accuracy. In comparison, the GRU-OL-MRT model yielded values of 1.73, 7.82, 2.54, and 0.917 for the same four metrics, with an average prediction time of 0.496 seconds per instance, significantly reducing computational costs while maintaining relatively high prediction accuracy.

, authors=Yufeng TANG^{1, 2}, Liqiu HE¹, Qingyuan XIONG², Yaling XIONG², Yanqiu SHI², Guangzhong HU^{**, 1}, authorsList=Yufeng TANG, Liqiu HE, Qingyuan XIONG, Yaling XIONG, Yanqiu SHI, Guangzhong HU, authorCompany=null, correspAuthors=Guangzhong HU, 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=1282037405733598070, articleId=1282037405054120818, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于PSO-GRU-OL-MRT的滑坡位移实时预测模型, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

针对静态预测模型难以满足动态滑坡趋势的准确预测，而动态预测模型往往带来计算成本的增加，给实时预测带来困难的问题，提出一种基于粒子群优化算法（PSO）-门控循环单元（GRU）-在线学习（OL）-动态模型重构（MRT）的滑坡位移实时预测模型。首先，结合PSO和GRU建立静态滑坡预测模型；其次，在静态模型基础上融入OL策略，在获取最新监测数据后动态更新和实时预测模型；然后，结合预测精度评价进行小批量MRT，动态实时预测滑坡趋势；最后，以四川省王耳岩滑坡体为例对比分析多个相关模型。结果表明：OL和MRT法可显著提升预测精度，其中PSO-GRU-OL-MRT模型的平均绝对误差（MAE）、绝对百分比误差（MAPE）、均方根误差（RMSE）、拟合优度R²指标分别为0.795、3.53、1.40、0.954，平均每次预测时间为25.0 s，具有最高的预测精度；而GRU-OL-MRT模型4个指标分别为1.73、7.82、2.54、0.917，平均每次预测时间为0.496 s，在具有较高预测精度的前提下大幅减少了计算成本。

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²四川远德安防检测设备有限公司，四川 自贡 643000, bio={"content":"

唐宇峰（1986—），男，四川宜宾人，博士，副教授，主要从事地质灾害力学分析、深度学习及其在地质灾害监测等方面的研究。E-mail: 386426034@qq.com。

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唐宇峰（1986—），男，四川宜宾人，博士，副教授，主要从事地质灾害力学分析、深度学习及其在地质灾害监测等方面的研究。E-mail: 386426034@qq.com。

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