Article(id=1223202679715909778, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223202678788965355, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20230267, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1677427200000, receivedDateStr=2023-02-27, revisedDate=1679846400000, revisedDateStr=2023-03-27, acceptedDate=null, acceptedDateStr=null, onlineDate=1769563844850, onlineDateStr=2026-01-28, pubDate=1703433600000, pubDateStr=2023-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769563844850, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769563844850, creator=13701087609, updateTime=1769563844850, updator=13701087609, issue=Issue{id=1223202678788965355, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='12', 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=1769563844630, creator=13701087609, updateTime=1769563913308, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223202966899901286, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223202678788965355, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223202966899901287, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223202678788965355, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=28, endPage=31, ext={EN=ArticleExt(id=1223202680005316756, articleId=1223202679715909778, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Prediction of Nanjing Water Consumption Based on Neural Network, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

It is of great significance for the long-term planning and allocation of Nanjing water resources to analyze the current situation of Nanjing water consumption, make reasonable prediction of Nanjing water consumption and master the future water demand. The analysis of water consumption in Nanjing from 2009 to 2019 showed that industrial and agricultural water consumption accounted for a large proportion of total water consumption in Nanjing, which played a crucial role in the change of total water consumption. The combined model of grey GM(1,1) model and Elman neural network was used to forecast the water consumption of all districts and the total water consumption of Nanjing. The results show that the grey Elman neural network model has a good prediction effect on the total water consumption of Nanjing City from 2009 to 2019. The relative errors of the forecasts were all less than 3.5%, and the average relative errors of the predicted results over the years were 1.55%; The relative error of the forecast results is less than 8.5% in the forecast of the water consumption of all districts in Nanjing in 2019. The model used in this paper can accurately predict the water consumption of Nanjing, which is of great significance to effectively control the regional water consumption and realize the principle of "four water and four determinations".

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分析南京市用水量现状，合理预测南京市用水量，掌握未来用水需求，对南京市水资源的长远规划和配置具有重大意义。对南京市2009~2019年用水量分析表明，工业、农业用水在南京市总用水量中比重较大，对总用水量的变化起着至关重要的作用。其次，采用灰色预测GM（1，1）模型与Elman神经网络的组合模型预测南京市各区及全市总用水量。结果表明，灰色Elman神经网络模型对2009~2019年南京市全市总用水量的预测效果良好，预测的相对误差均小于3.5%，预测结果的多年平均相对误差为1.55%；在南京市2019年各区用水量预测中，预测结果的相对误差均小于8.5%，效果较好。可见所用模型能准确地预测南京市用水量，对有效控制区域用水量，实现“四水四定”原则具有重要意义。

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^1.b.南京信息工程大学应用气象学院江苏省农业气象重点实验室，江苏 南京 210044, bio={"content":"

王浏琳（2000-），女，硕士研究生，研究方向为应用气象学，E-mail：1830373339@qq.com

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王浏琳（2000-），女，硕士研究生，研究方向为应用气象学，E-mail：1830373339@qq.com

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