Article(id=1223196809862955991, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20221477, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1658073600000, receivedDateStr=2022-07-18, revisedDate=1664553600000, revisedDateStr=2022-10-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1769562445368, onlineDateStr=2026-01-28, pubDate=1692892800000, pubDateStr=2023-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769562445368, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769562445368, creator=13701087609, updateTime=1769562445368, updator=13701087609, issue=Issue{id=1223196803663778281, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='8', pageStart='1', pageEnd='222', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769562443888, creator=13701087609, updateTime=1769563793740, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223202465391166440, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223202465391166441, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223196803663778281, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=77, endPage=80, ext={EN=ArticleExt(id=1223196814317306107, articleId=1223196809862955991, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Flood Forecasting Model Based on Similarity Measurement of Spatio-temporal Hydrological Data, columnId=1222925283431272938, journalTitle=Water Resources and Power, columnName=HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING, runingTitle=null, highlight=null, articleAbstract=

In the case that hydrological data only include rainfall station data and measured water level data of river monitoring section, it is always a major challenge to improve the accuracy of flood forecasting in small and medium-sized basins. In this paper, flood automatic coding was realized according to data characteristics, and historical floods were classified by using decision tree model, thus the candidate flood groups and eliminated flood groups were obtained and the calculation efficiency was improved. On this basis, a flood forecasting model was established based on the similarity measurement of spatio-temporal hydrological data. Taking the hydrological data of Dafenshui Basin in Jiangxi Province as an example, a case analysis of hydrological data was carried out, and four floods were randomly selected for verification. The results show that the qualification rate of flood peak water level is 100%, and the qualification rate of flood peak time is 75%, the accuracy is high, which has important theoretical value and practical significance in hydrological data research.

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在水文数据只包括雨量站数据和河道监测断面水位实测数据的情况下，提高中小型流域洪水预报精度一直是一个重大挑战。根据数据特征实现洪水的自动化编码，利用决策树模型对历史洪水分类，得到候选洪水组和淘汰洪水组，提高计算效率。在此基础上，建立基于时空水文数据相似性度量的洪水预报模型，以江西省大汾水流域水文数据为例开展水文数据分析，并随机选取四场洪水进行验证，结果表明洪峰水位合格率为100%，峰现时间合格率为75%，精度较高，在水文数据研究中具有重要理论价值和现实意义。

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谢敏（1985-），男，硕士、高级工程师，研究方向为水利信息化，E-mail：xm@jxsl.gov.cn

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谢敏（1985-），男，硕士、高级工程师，研究方向为水利信息化，E-mail：xm@jxsl.gov.cn

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