Dam deformation prediction method considering spatiotemporal correlation of multiple measurement points based on LSTM-Transformer

Traditional dam deformation prediction methods tend to ignore the influence of deformation in neighbouring areas on monitoring points and face the problem of long‐ and short‐term dependence, resulting in less accurate prediction results. For this reason, this paper proposes a dam deformation prediction method based on long short‐term memory and Transformer (LSTM-Transformer) that considers the spatiotemporal correlation of multiple measurement points. Firstly, the spatiotemporal correlation points related to the deformation of the monitoring points are mined using association rules and a dam deformation prediction model considering the spatiotemporal correlation of multiple measurement points is constructed based on the consideration of the influence of environmental factors. Secondly, the fusion of LSTM with Transformer enables the model to better capture global information in the sequence, which solves the short‐ and long‐term dependence problems in the dam deformation prediction model. Finally, the universality of the model and the superiority of the method is verified through two experiments.