水利水电技术（中英文）2026，Vol.57Issue(5)：110-120,11.DOI:10.13928/j.cnki.wrahe.2026.05.009
基于CNN-LSTM-AM模型的沿黄九省(区)用水量预测
Water consumption prediction in nine provinces(regions)along Yellow River based on CNN-LSTM-AM model
摘要
Abstract
[Objective]To address the limitations of unsystematic integration of natural and social dual characteristics and insufficient modeling of spatiotemporal heterogeneity in water consumption prediction,a spatiotemporal collaborative prediction framework for water consumption in the nine provinces and regions along the Yellow River is constructed based on deep learning method.[Methods]A preliminary dataset was constructed using 29 characteristic factors influencing water consumption.The importance of these factors was ranked using the random forest algorithm,and redundant features were eliminated.Considering the characteristics and applicable scenarios of different deep learning algorithms,a hybrid prediction model based on convolutional neural network(CNN),long short-term memory(LSTM)network,and attention mechanism(AM)was established and compared with other baseline models.To address the problem of extreme errors,a dual-attention collaborative mechanism was designed to optimize the model.[Results]In the study area,the CNN-LSTM-AM model achieved better simulation result than other models,with mean absolute error(MAE),mean absolute percentage error(MAPE),and root mean square error(RMSE)reduced by 7.7％～40.6％,22.6％～44.1％,and 0.7％～32.1％,respectively,indicating superior overall performance.After introducing the dual-attention collaborative mechanism,extreme errors were reduced while maintaining small fluctuations in overall accuracy.The model demonstrated good generalization ability and was able to predict future water consumption in the study area with high accuracy.[Conclusion]In the study area,the current model shows good applicability and prediction accuracy,providing a new technical approach for spatiotemporal collaborative prediction of water consumption.Future research should consider the balance among model adaptability,complexity,and stability based on task requirements,and construct a comprehensive prediction system through multi-dimensional analysis.关键词
深度学习/用水量预测/卷积神经网络/长短期记忆网络/注意力机制/自然-社会二元特征/时空异质性/水资源规划Key words
deep learning/water consumption prediction/convolutional neural network/long short-term memory network/attention mechanism/naturac-social binary features/spatio temporal heterogeneity/water tesources planning分类
建筑与水利引用本文复制引用
许非凡,杜军凯,张诚,王慧亮,仇亚琴,刘玥晓,陈鑫..基于CNN-LSTM-AM模型的沿黄九省(区)用水量预测[J].水利水电技术（中英文）,2026,57(5):110-120,11.基金项目
国家重点研发计划(2023YFF1304202) （2023YFF1304202）
流域水循环模拟与调控国家重点实验室项目(SKL2024YJZD02) （SKL2024YJZD02）
云南省重点研发计划项目(202303AC100020) （202303AC100020）
