华中科技大学学报（自然科学版）2026，Vol.54Issue(7)：27-33,7.DOI:10.13245/j.hust.250280
耦合CNN-SE Attention-GRU机制的中长期径流智能预测研究
Mid-long term runoff intelligent prediction research based on CNN-SE Attention-GRU coupling mechanism
摘要
Abstract
In order to improve the effect of the mid-long term runoff forecasting,a deep learning model combining convolutional neural networks(CNN)model,gated recurrent unit(GRU)and squeeze-and-excitation attention(SE Attention)was proposed.The model extracts spatial features of hydrometeorological data through CNN,learns long-term temporal patterns via GRU,and enhances the influence of key information using SE Attention,thereby constructing a CNN-SE Attention-GRU hybrid model for medium-and long-term runoff forecasting.The study was conducted in the Futou Lake basin,employing hydrometeorological data from 1971 to 2017 to perform multivariate runoff predictions at the Jinkou and Shihaoqiao stations.The proposed CNN-SE Attention-GRU model was compared with single models(LSTM,GRU),hybrid models(CNN-LSTM,CNN-GRU),and a composite model with the same architecture(CNN-SE Attention-LSTM).The results demonstrate that the CNN-SE Attention-GRU model exhibiting optimal performance,the model attained root mean square error(RMSE)values of 17.86 and 5.18,mean absolute error(MAE)values of 10.50 and 3.10,mean absolute percentage error(MAPE)values of 15.03％and 15.15％,coefficient of determination R2 values of 0.765 and 0.874,and Nash efficiency coefficient(NSE)values of 0.79 and 0.88,respectively.关键词
智慧水文/中长期径流预测/卷积神经网络/门控循环单元/注意力机制/斧头湖流域Key words
smart hydrlogy/mid-long term runoff forecasting/convolutional neural networks/gated recurrent unit/SE Attention/Futou Lake basin分类
建筑与水利引用本文复制引用
黎育红,郝舒哲,陆佳俊,张勇传..耦合CNN-SE Attention-GRU机制的中长期径流智能预测研究[J].华中科技大学学报（自然科学版）,2026,54(7):27-33,7.基金项目
湖北省水利重点科研项目(HBSLKY202325). （HBSLKY202325）
