人民黄河2026，Vol.48Issue(5)：50-58,71,10.DOI:10.3969/j.issn.1000-1379.2026.05.008
基于耦合预测模型的大通河月径流预测
Prediction of Monthly Runoff in the Datong River Based on a Coupled Prediction Model
摘要
Abstract
The simulation and prediction of river runoff are of great significance for controlling basin water volume and ensuring optimal allo-cation of basin water resources.However,due to the influence of abnormal climate and human activities,the instability of medium-and long-term runoff sequences has increased the difficulty of runoff prediction.To improve prediction accuracy,a coupled deep learning model frame-work based on variational mode decomposition(VMD),mutual information(MI),and bidirectional long short-term memory(Bi-LSTM)networks,called the VMD-Bi-LSTM model,was established.First,VMD was used to decompose the original runoff data into intrinsic mode components;Then,Bi-LSTM was applied to each component to build prediction models,with the input lag time determined by the mutual in-formation method;Finally,the prediction results of each subsequence were superimposed to obtain the final prediction result.The paper ex-plored the performance of the proposed model in predicting the monthly runoff at Tiantang hydrological station in the Datong River Basin and compared it with other models.The results show that:Compared to other models,this model exhibits significant advantages in both point and interval predictions.The Nash-Sutcliffe efficiency coefficient(NSE)of the prediction results reaches 0.95,and the coverage rates of interval predictions are 0.92 and 0.85 at the 95%and 90%confidence intervals,respectively.关键词
VMD/径流预测/Bi-LSTM/区间预测/大通河Key words
VMD/runoff prediction/Bi-LSTM/interval prediction/Datong River分类
天文与地球科学引用本文复制引用
肖萍,董国涛..基于耦合预测模型的大通河月径流预测[J].人民黄河,2026,48(5):50-58,71,10.基金项目
国家社会科学基金资助项目(23&ZD104) （23&ZD104）
