Article(id=1284878799355483114, tenantId=1146029695717560320, journalId=1283840460791746584, issueId=1284096873799594072, articleNumber=PA20260711_53zmJev5, orderNo=null, doi=10.13637/j.issn.1009-6094.2025.1337, 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=1784268577660, onlineDateStr=2026-07-17, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784268577659, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784268577659, creator=admin, updateTime=1784268577659, updator=admin, issue=Issue{id=1284096873799594072, tenantId=1146029695717560320, journalId=1283840460791746584, year='2026', volume='26', 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SVR)插补方法。通过分阶段网格搜索与5折交叉验证优化模型超参数，并将插补结果与常规线性插值方法进行对比。结果显示，SVR模型在高锰酸盐指数、pH值和总硬度的插补精度上均优于线性插值，表明其适用于样本量小、缺失率高、连续缺失多的水质时间序列数据；锰质量浓度受检出限影响其插补效果略低于线性插值，未来需单独分析该类缺失问题。研究为地下水监测数据缺失值处理提供了可靠的新方法，具有较强的实际应用价值。, 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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