河海大学学报（自然科学版）2026，Vol.54Issue(2)：38-44,71,8.DOI:10.3876/j.issn.1000-1980.2026.02.005
基于多模型神经网络的径流缺失数据重建
Reconstruction of missing runoff data based on multi-model neural networks
摘要
Abstract
To address the problem of missing runoff data,a daily runoff prediction model(MM-LSTM-BP model)combining a long short-term memory(LSTM)neural network and a back propagation(BP)neural network based on multiple regression models was constructed.In this model,regression models were adopted to extract the linear,nonlinear,temporal,and random characteristics of runoff,and the LSTM neural network and the BP neural network were used in series for the regression simulation of the daily runoff process.The case verification results of the Weihe River Basin indicate that the MM-LSTM-BP model generally performs better than the single regression methods during the verification period;the root mean square error(RMSE)of the daily runoff data decreases by more than 50%,and the Nash efficiency coefficient(NSE)increases to 0.935;the MM-LSTM-BP model has better stability during the normal flow period and the recession period,and the simulation error of the flood peak is reduced by more than 6%during the flood period.关键词
径流回归/深度学习/LSTM神经网络/BP神经网络/渭河流域Key words
runoff regression/deep learning/LSTM neural network/BP neural network/the Weihe River Basin引用本文复制引用
管亚硕,连炎清,金君良,张佳鹏,任玉玲..基于多模型神经网络的径流缺失数据重建[J].河海大学学报（自然科学版）,2026,54(2):38-44,71,8.基金项目
科技部重点研发计划项目(2021YFC3201100) （2021YFC3201100）
陕西省科技厅重点研发项目(2020KWZ-023) （2020KWZ-023）
