中国水利Issue(8)：61-66,72,7.DOI:10.3969/j.issn.1000-1123.2025.08.011
基于CNN-LSTM-Attention融合模型的东江流域中长期水文预报方法及应用
A CNN-LSTM-Attention fusion model for medium to long range hydrological forecast and its application in the Dongjiang River Basin
彭海波 1戴善进 1李泽华 2钟华 1利逸 2刘菁 1田兆伟 1徐飞2
作者信息
- 1. 广东省水文局惠州水文分局,516003,惠州
- 2. 广东省科学院广州地理研究所,510070,广州||广东省地理空间信息技术与应用公共实验室,510070,广州||广东省遥感与地理信息系统应用重点实验室,510070,广州
- 折叠
摘要
Abstract
To address the challenges of long lead times,complex influencing factors,and high uncertainty in medium-to long range hydrological forecast,this study proposes a deep learning-based hydrological forecast method.The method extracts time-varying features of antecedent climate indices using convolutional neural networks(CNN),captures temporal dependencies of hydrological processes with long short-term memory networks(LSTM),and focuses on key abrupt signals through a multi-head attention mechanism(Multi-Head Attention),thereby dynamically correcting the European Centre for Medium-Range Weather Forecasts(ECMWF)SEAS5 numerical forecast products.Taking the Dongjiang River Basin as a case study,a CNN-LSTM-Attention deep learning fusion model was constructed using monthly hydrological and meteorological observation data from 1993 to 2022.The results show that for lead times of 1～3 months,the model significantly improves precipitation forecast accuracy,reducing the mean absolute error(MAE)by 0.6～5.5 mm and increasing the coefficient of determination(R²)by 0.10～0.11 compared to the SEAS5 ensemble mean forecast.In terms of runoff,the MAE is reduced by 0.4～5.7 m³/s and R² is improved by 0.07～0.31 for lead times of 1～5 months.Additionally,the developed model integration and visualization platform offers an operationally valuable solution for medium-to long range hydrological forecast in the Dongjiang River Basin.关键词
东江流域/深度学习/卷积神经网络/长短期记忆网络/注意力机制Key words
Dongjiang River Basin/deep learning/convolution neural network/long short-term memory/attention mechanism分类
建筑与水利引用本文复制引用
彭海波,戴善进,李泽华,钟华,利逸,刘菁,田兆伟,徐飞..基于CNN-LSTM-Attention融合模型的东江流域中长期水文预报方法及应用[J].中国水利,2025,(8):61-66,72,7.
