A Dam Displacement Prediction Method Based on a Model Combining Random Forest, a Convolutional Neural Network, and a Residual Attention Informer

To enhance the accuracy of dam displacement prediction, this paper proposes a hybrid model combining Random Forest (RF), a Convolutional Neural Network (CNN), and a Residual Attention Informer (RA-Informer). Firstly, RF is utilized to assess the importance of input features, selecting key factors that significantly influence dam displacement. Then, CNN is employed to perform deep feature extraction on the input data, mining effective information. Subsequently, the Informer model integrated with a residual attention mechanism establishes the mapping relationship between the extracted features and dam displacement, enhancing the focus on critical features. Finally, the Northern Goshawk Optimization (NGO) algorithm is adopted to optimize the model’s hyperparameters. Experimental results on actual engineering data demonstrate that the proposed model exhibits superior prediction accuracy and stability compared to other typical models, offering higher precision and reliability.