Article(id=1284878784272765849, tenantId=1146029695717560320, journalId=1283840460791746584, issueId=1284096873799594072, articleNumber=PA20260711_mSI4jIJp, orderNo=null, doi=10.13637/j.issn.1009-6094.2025.1125, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1784268574064, onlineDateStr=2026-07-17, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784268574063, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784268574064, creator=admin, updateTime=1784268574064, updator=admin, issue=Issue{id=1284096873799594072, tenantId=1146029695717560320, journalId=1283840460791746584, year='2026', volume='26', issue='6', pageStart='null', pageEnd='null', issueExtLink='null', onlineDate='null', pubDate='1782316800000', pubDateStr='2026-06-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1784082152080, creator='ztmeta', updateTime=1784082152080, updator='ztmeta', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=null, endPage=null, ext={CN=ArticleExt(id=1284878788068610974, articleId=1284878784272765849, tenantId=1146029695717560320, journalId=1283840460791746584, language=CN, title=基于RF特征优选的SSA-SVM的铁路桥涵洪灾风险评价方法研究, columnId=null, journalTitle=安全与环境学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=随着我国西部铁路线路的建设，运营铁路线路的洪灾风险问题也不断突出，急需减小洪灾风险且提高防洪减灾效率。针对河西走廊漫流区铁路桥涵洪灾，研究基于麻雀搜索算法(Sparrow Search Algorithm, SSA)优化支持向量机模型(Support Vector Machine, SVM)以开展洪灾风险评价。首先，针对致灾因子、孕灾环境、承灾体(桥涵),搜集11个山洪影响因素；其次，采用随机森林(Random Forest, RF)算法进行重要度排序，并选出7个主要的影响因素作为模型特征值；再次，运用SSA算法优化SVM模型参数，建立基于麻雀搜索算法优化的支持向量机风险评价模型；最后，选取准确度、精确度、召回率和F_(1-score)共4个指标，将SSA-SVM模型与传统SVM模型、粒子群优化算法(Particle Swarm Optimization, PSO)优化SVM模型、鲸鱼优化算法(Whale Optimization Algorithm, WOA)优化SVM模型进行对比分析。研究结果显示：构建的SSA-SVM洪灾风险评价模型验证集的精确度、F_(1-score)分别为96.67%、96.91%,而且明显优于SVM、PSO-SVM、WOA-SVM模型，不仅能够实现铁路桥涵洪灾风险综合评价效能，还能为类似山洪风险评价提供决策支持。, authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, 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