基于LSTM和KNN组合模型的黄河源区日径流量模拟研究
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1.青海省气象科学研究所,青海 西宁810001;2.青海省防灾减灾重点实验室,青海 西宁810001;3.青海省气象服务中心,青海 西宁810001

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暖湿化进程中三江源区气候-水文-生态过程的动态耦合机制及其径流效应(U22A20556);第二次青藏高原科考项目(2019QZKK0105);青海省温室气体及碳中和重点实验室开放基金项目(MSXM-2023-02)


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Simulation Studies of Daily Runoff in the Source of the Yellow River Based on the Combined Model of LSTM and KNN
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1.Qinghai Institute of Meteorological Sciences,Xining;2.Qinghai Key Laboratory of Disaster Prevention and Reduction,Xining;3.Qinghai Meteorological Service Center,Xining

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    摘要:

    本文提出了长短期记忆网络(LSTM)和K近邻算法(KNN)组合模型对水文流量进行预报方法,对黄河源区的吉迈、军功、唐乃亥三个水文断面的日径流量进行预报分析。首先用温度、降水等气象要素构建流域动态属性,用历史气象水文和地理信息资料构建流域静态属性特征,用LSTM模型开展特征优选,确定最优模型TOPO_CLIM_SOIL-LSTM后用于实际的日径流量预测,然后用KNN算法对预测结果进行实时校正。结果表明,具有流域属性特征的TOPO_CLIM_SOIL-LSTM模型能更好地学习到降雨与径流之间的关系,可有效解决低流量段预测径流量跳变的问题,用KNN模型对预测流量修订后,吉迈、军功、唐乃亥站未来1日的日径流量预报准确率均达到了93%以上,纳什系数分别提高了18.07%、6.45%和12.5%,有效提高日径流量预报精度。

    Abstract:

    This article proposes a combined Long Short Term Memory Network(LSTM) and K-Nearest Neighbor (KNN) model for predicting hydrological flow, and analyzes the daily runoff of the Jimai,Jungong and Tangnaihai hydrological sections in the source area of the Yellow River.First,the dynamic attributes of the watershed are constructed by using meteorological factors such as temperature and precipitation,Using historical meteorological and hydrological and geographic information data to build the static attribute characteristics of river basin,The LSTM model is used for feature optimization,Determine the optimal model TOPO_ CLIM_ SOIL-LSTM, it is used for actual daily runoff forecast,Then the KNN algorithm is used to correct the prediction results in real time.The results show that,Model TOPO_CLIM_SOIL-LSTM with river basin attribute characteristics can better learn the relationship between rainfall and runoff,It can effectively solve the problem of predicting runoff jump in low flow section,After revising the predicted flow with KNN model,The accuracy rate of daily runoff forecast at Jimai,Jungong and Tangnaihai stations in the next day has reached over 93%,Nash coefficient increased by 6.45%,12.5% and 18.07% respectively,Effectively improve the accuracy of daily runoff forecast.

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  • 收稿日期:2023-10-13
  • 最后修改日期:2024-11-13
  • 录用日期:2024-09-11
  • 在线发布日期: 2025-04-10
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