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基于多因素相似性的融雪径流预报方法研究
引用本文:闻昕,陈然,谭乔凤,施颖,丁紫玉.基于多因素相似性的融雪径流预报方法研究[J].水力发电学报,2022,41(3):46-59.
作者姓名:闻昕  陈然  谭乔凤  施颖  丁紫玉
摘    要:融雪径流是高寒山区水循环的重要组成,其预报对流域水资源综合利用具有重要意义.本文基于流域产汇流机理和冰川积雪融化相关研究,融合物理成因分析和数据挖掘技术的优势,建立基于多因素相似性的融雪径流预报模型,并提出滚动预报方案,实现了7日预见期内逐日径流滚动预报.研究成果在雅砻江干流新龙站的应用表明:对于考虑正积温方案,3 d...

关 键 词:融雪径流  滚动预报  相似性  数据驱动

Study on forecasting method of snowmelt runoff based on multi-factor similarity
WEN Xin,CHEN Ran,TAN Qiaofeng,SHI Ying,DING Ziyu.Study on forecasting method of snowmelt runoff based on multi-factor similarity[J].Journal of Hydroelectric Engineering,2022,41(3):46-59.
Authors:WEN Xin  CHEN Ran  TAN Qiaofeng  SHI Ying  DING Ziyu
Abstract:Snowmelt runoff is an important component of the water cycle in alpine areas; its forecast is of great significance to the comprehensive utilization of water resources in a basin. Based on previous studies on watershed confluence mechanism and glacier snow melting, this paper develops a snowmelt runoff forecast model based on the similarity of multiple factors by combining the advantages of physical cause analysis and data mining technology, and works out a plan to achieve a 7-day rolling forecast of daily runoff. Application in a case study of the Xinlong station on the Yalong River shows that this model has an average relative error lower than 17% over the 3 d forecast period, and its Nash coefficient reaches 0.89 for schemes with accumulated positive temperature. For the 7 d forecasts, the error is lower than 21% and the coefficient up to 0.83. This means an error reduction by 2% and 6% and a Nash coefficient increase by 0.03 and 0.08 for the 3 d and 7 d forecasts, respectively, relative to the schemes without accumulated positive temperature. Our method can mine quantitatively the experience of referring to the past and forecasting the future from front-line business personnel, and provide interpretable runoff forecast results, significantly improving runoff forecast accuracy and extending forecast periods.
Keywords:Snowmelt runoff  rolling forecast  similarity  data-driven    
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