水科学进展2025，Vol.36Issue(2)：217-229,13.DOI:10.14042/j.cnki.32.1309.2025.02.004
基于物理机制耦合深度学习的黄河干流流量演进模拟
Simulation of river flow evolution in the Yellow River main stream based on the coupling of physical mechanisms and deep learning
摘要
Abstract
Evolution of river flow is vital for water regulation in the Yellow River basin.There is an urgent need for a high-precision,low-latency simulation model that considers an artificial lateral water cycle to meet the demand for accurate and refined water regulation from water sources to end-users.Based on the analysis of unbalanced water volume in the Yellow River's main stream,this study used the CNN-LSTM algorithm to build flow evolution models.The SCE-UA algorithm was used to tune the parameters.The"store-wet-release-dry"mechanism of the Longyangxia Reservoir was embedded in the globally-calibrated CNN-LSTM model for simulation.Model comparison indicated the following:① Unbalanced water volume in the main stream is in the order of middle＞lower＞upper reaches,with no significant sub-interval correlation.② The comprehensive evaluation indicators(ER-R-M)mean values of the hydrological method,CNN-LSTM(local optimization),CNN-LSTM(global optimization),and the coupled simulation were 0.627,0.613,0.774,and 0.811 respectively.The upper and lower reaches had higher accuracy than the middle reaches.③ Building a deep neural network guided by physical simulation can effectively limit the solution space,with 29.3％higher accuracy than the hydrological method,which has practical value for water regulation in the Yellow River.关键词
流量演进/不平衡水量/物理机制/深度学习/黄河干流Key words
evolution of river flow/unbalanced water volume/physical mechanism/deep learning/Yellow River's main stream分类
水利科学引用本文复制引用
罗赟,张翔宇,董增川,李强坤,左其亭,韩金旭,周康军..基于物理机制耦合深度学习的黄河干流流量演进模拟[J].水科学进展,2025,36(2):217-229,13.基金项目
国家重点研发计划项目(2023YFC3206701 （）
2024YFC3211304) The study is financially supported by the National Key R&D Program of China(No.2023YFC3206701 （）
No.2024YFC3211304). （）
