基于CNN-LSTM-AM模型的沿黄九省(区)用水量预测OA

[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.