Article(id=1223190868341932747, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20230294, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1677600000000, receivedDateStr=2023-03-01, revisedDate=1681056000000, revisedDateStr=2023-04-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561028799, onlineDateStr=2026-01-28, pubDate=1695571200000, pubDateStr=2023-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561028799, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561028799, creator=13701087609, updateTime=1769561028799, updator=13701087609, issue=Issue{id=1223190866320278179, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='9', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769561028318, creator=13701087609, updateTime=1769562015483, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223195006870082478, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223195006870082479, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223190866320278179, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=36, endPage=39, ext={EN=ArticleExt(id=1223190870007071464, articleId=1223190868341932747, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Comparison of Evaluation Methods for Eutrophication of Water Quality in Honghu Lake, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

To scientifically evaluate the eutrophication degree of water bodies, based on the comparison and analysis of various eutrophication evaluation methods, convolutional neural network (CNN) was introduced to establish the convolutional-eutrophication (CNN-E) model. Based on the monthly-scale water quality and algae monitoring data of Honghu Lake from 2014 to 2019, the comprehensive nutrient index method, BP neural network method and CNN-E model were used to evaluate its eutrophication degree. The mean absolute error, root mean square error, coefficient of determination and Nash-Sutcliffe efficiency coefficient were used to evaluate the performance of the neural network model. The results show that Hong Lake was in a mild eutrophic state for a long time, and the eutrophication level was increasing. In terms of model performance, the four evaluation indexes of CNN-E model are better than BP neural network 0.166, 0.098, 0.078 and 0.087, respectively. The CNN-E model can provide technical support for the prevention and comprehensive management of eutrophication in lake water bodies.

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为科学评价水体富营养化程度，在对比分析各种富营养化评价方法基础上，引入卷积神经网络（CNN），建立卷积-富营养化（CNN-E）模型。根据洪湖2014~2019年月尺度水质及藻类监测数据，采用综合营养指数法、BP神经网络法与CNN-E模型评价其富营养化程度，并采用平均绝对误差、均方根误差、决定系数和纳什效率系数评价神经网络模型性能。结果表明，洪湖长期处于轻度富营养状态，富营养化程度不断加重。模型性能方面，CNN-E模型四种评价指标分别优于BP神经网络0.166、0.098、0.078与0.087。CNN-E模型可为湖泊水体富营养化预防及综合治理提供技术支持。

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孙咏曦（1997-），男，硕士研究生，研究方向为水文水资源，E-mail：973743274@qq.com

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孙咏曦（1997-），男，硕士研究生，研究方向为水文水资源，E-mail：973743274@qq.com

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