Hierarchical prediction of dam deformation based on hybrid temporal network and load-oriented residual correction

Abstract

Dam deformation prediction methods try to make the predicted values infinitely close to the true values, but no method can capture all the deformation information and residuals are inevitable. For this reason, this paper develops a hierarchical dam deformation prediction method centred on residual correction strategy combined with hybrid temporal network. Firstly, hybrid CNN-LSTM is established based on convolutional neural network (CNN) and long short-term memory network (LSTM), aiming at mining the deformation temporal features and outputting the deformation trend information. Secondly, in the training stage, the mapping relationship between environmental loads and residuals is established by using Extreme Learning Machine (ELM), and the load information embedded in the residuals is mined to output the fluctuation information specific to dam deformation. Then, in the prediction stage, the preliminary deformation prediction results are outputted by CNN-LSTM, and the residual correction values are obtained according to the mapping relationship between environmental loads and residuals, so as to obtain the prediction results. The analysis shows that the outputs of CNN-LSTM can reflect the deformation trend term and provide reliable preliminary prediction results for residual correction, the ELM mapping relation model can capture the loading information embedded in the residuals that cannot be mined by the temporal model, which improves the interpretability of residual correction, and the output results reflect the deformation fluctuations well. The proposed method can realise the adaptive correction of the preliminary deformation prediction results, and the prediction accuracy is satisfactory and adaptable.