基于CNN-LSTM-Attention和自回归的混合水位预测模型OA北大核心CSTPCD

[Objective]Water level forecasting in multivariate time series is crucial for various applications such as transportation,agriculture,and flood control.Accurate prediction of water levels in the Xijiang River is essential for enhancing the safety and efficiency of waterway transportation,reducing flood risks,and promoting sustainable development in the region.However,water level forecasting involves a combination of linear and nonlinear problems,which traditional method like autoregressive and ARI-MA models may struggle to handle effectively.[Methods]To address this challenge,a novel hybrid water level forecasting model called the Convolutional Recurrent Attention Autoregressive network(CRANet)is proposed.The strengths of Convolutional Neu-ral Network(CNN),Long Short-Term Memory(LSTM),Attention mechanism,and Autoregressive(AR)component are com-bined by CRANet.By integrating these components,both local and global dependencies within the water level dataset are effi-ciently captured by CRANet.Spatial and temporal patterns are excellently captured by the CNN and LSTM components,while the time-series nature of the data is accounted for by the AR component.Furthermore,the model's ability to prioritize relevant fea-tures is enhanced by the attention mechanism,leading to further improvements in its forecasting performance.[Results]The pro-posed CRANet model has been successfully applied to water level forecasting at Wuzhou Station in the Xijiang River,China.On the test set,the MAE,RMSE,and R2 for forecasting future water levels at a 3-hour interval are observed to be 0.086,0.1145,and 0.9508,respectively.[Conclusion]The result indicate that the proposed CRANet model demonstrates high availability,accuracy,and robustness in water level forecasting,exhibiting superior MAE,RMSE,and R2 compared to other baseline models such as AR,SVR,CNN,LSTM and et al.