Accurate forecasting of the evolution of deformation patterns of a concrete dam is critical to ensuring its safe operation and facilitating long-term maintenance. However

conventional prediction models often suffer from insufficient accuracy and poor interpretability in dealing with nonlinear

multi-dimensional

and long-term deformation time series data. In this work

the Convolutional Neural Network (CNN) is adopted to extract local spatial features from deformation data; the Inverted Transformer (iTransformer) with its multi-head attention mechanism are used to capture long-time series dependencies. And

we use Long Short-Term Memory (LSTM) to enhance the memory of time series data

and optimize globally the key model hyperparameters using the Particle Swarm Optimization algorithm (PSO). Then

we develop an interpretable hybrid model integrating PSO

CNN

iTransformer

and LSTM

and conduct global sensitivity analysis to explain the model’s results. We compare and verify multiple gauge points and multiple models in a case study involving 11 years of displacement measurements on the Xiluodu large concrete arch dam

with model performance assessed via the root mean square error (RMSE) metric. The results show our new model achieves more accurate predictions of the dam deformation trend

featured with higher accuracy

stronger generalization ability

and better stability

and good interpretability.