Article(id=1223278133172895958, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222548, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1670342400000, receivedDateStr=2022-12-07, revisedDate=1672934400000, revisedDateStr=2023-01-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1769581834356, onlineDateStr=2026-01-28, pubDate=1698163200000, pubDateStr=2023-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769581834356, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769581834356, creator=13701087609, updateTime=1769581834356, updator=13701087609, issue=Issue{id=1223278131956551924, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='10', pageStart='1', pageEnd='228', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769581834067, creator=13701087609, updateTime=1769584342407, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223288652869030422, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223288652869030423, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223278131956551924, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=50, endPage=53, ext={EN=ArticleExt(id=1223278133944647903, articleId=1223278133172895958, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Prediction of Electrical Conductivity in Sub-lake of Poyang Lake Based on Random Forest Regression Model and High-frequency Data, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

Conductivity is an important parameter to measure water quality. High-frequency monitoring of water conductivity plays an important role in water quality management. Due to the complexity of field conditions, equipment failure often leads to data loss. In order to improve the high-frequency monitoring data, machine learning model was used to predict the conductivity content in water body based on the meteorological and physical indexes obtained from high-frequency monitoring. The results show that the random forest regression model has the best prediction effect, with its determination coefficient R² reaching 0.996, root mean square error (R_RMSE) 1.31 μS/cm, and mean relative error (M MRE) 0.38%. The pH value contributed the most and was the dominant factor affecting the conductivity. The results are conducive to optimizing the field high-frequency monitoring system platform, improving the high-frequency monitoring data, which provides scientific basis for water quality management.

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电导率是衡量水质的重要参数，高频监测获取水体中电导率对水质管理具有重要作用。由于野外条件的变化复杂性引起设备故障导致数据缺失时有发生，为优化野外监测体系和插补缺失数据，基于高频监测获取的气象和水体物理指标，结合机器学习模型，预测水体中电导率值。结果表明，随机森林回归模型预测效果最优，其决定系数R²可达0.996，均方根误差R_RMSE为1.31 μS/cm，平均相对误差M MRE为0.38%；pH值贡献率最大，是影响电导率的主导因素。研究结果利于优化野外高频监测系统平台，健全高频监测数据，为水质管理提供科学依据。

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刘丽贞（1987-），女，副研究员，研究方向为水环境生态，E-mail：woliulizhen2007@126.com

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