人民黄河2025，Vol.47Issue(4)：38-43,57,7.DOI:10.3969/j.issn.1000-1379.2025.04.007
黄河三门峡水文站非汛期流量预测研究
Study on Non-Flood Season Flow Prediction of Sanmenxia Hydrologic Station of the Yellow River
摘要
Abstract
The Yellow River runoff has unsteady and non-linear characteristics.In order to provide reference for ensuring water security in Henan Province,the non-flood season discharge of Sanmenxia Hydrology Station of the Yellow River was studied.The paper built non-flood season runoff prediction models by combining variational mode decomposition(VMD)with long short term memory(LSTM)and support vec-tor regression(SVR).The sparrow search algorithm(SSA)was used to adjust the model parameters to improve the prediction accuracy.The runoff data was decomposed into multiple eigenmode functions(IMF)by the VMD algorithm,Euclidean distance between components was calculated based on K-Means clustering method and the reciprocal of Euclidean distance was used as the weight of each component.Finally,the results of each component were put into LSTM/SVR for model prediction,and the runoff results were obtained by weighted reconstruction of the predicted values of components.Comparing with before and after weighted VMD-SSA-LSTM and VMD-SSA-SVR model,the results show that the proposed K-Means weighted VMD-SSA-LSTM model predicts the average daily runoff of Sanmenxia Hydrology Station from Jan-uary 2003 to May 2023(non-flood season month),with the mean absolute error being 82.54 m3/s,the root-mean-square error being 106.64 m3/s and the fitted coefficient being 0.92,the trend of runoff can be predicted more effectively.关键词
径流预测/变分模态分解/LSTM/SVR/K-Means聚类/黄河流域Key words
runoff prediction/variational mode decomposition/LSTM/SVR/K-Means cluster/Yellow River Basin分类
水利科学引用本文复制引用
程桂芳,周芸..黄河三门峡水文站非汛期流量预测研究[J].人民黄河,2025,47(4):38-43,57,7.基金项目
河南省高等教育教学改革研究与实践项目(2021SJGLX060) （2021SJGLX060）
河南省科技攻关项目(252102211117) （252102211117）
