Deformation serves as a crucial indicator of the structural changes of dams. Enhancing the prediction accuracy of dam deformation is of paramount significance for the safety and structural control of dams

due to the nonlinear characteristics of deformation data and the underlying intricate mechanism. This paper develops a combined approach for dam deformation prediction based on the integrated modeling concept

integrating an improved wavelet threshold denoising and a Pelican Optimization Algorithm (POA) optimized Bidirectional Long Short-Term Memory (BiLSTM) network. First

the deformation measurement data sequence is processed using an improved wavelet threshold denoising method; then

POA is used to search for the optimal hyperparameter combination to optimize the BiLSTM model; finally

dam deformation prediction is conducted based on the BiLSTM with the optimal hyperparameters. Engineering case studies demonstrate that this improved wavelet threshold method produces superior denoising effects

and POA-BiLSTM gives a satisfactory accuracy for dam deformation prediction. And on the ultimate test set

it has achieved the average MAE

MAPE

RMSE

and R2 of 0.244

0.041

0.301

and 0.906

respectively. Compared to other methods

it exhibits higher predictive accuracy and robustness

offering valuable insight for dam deformation monitoring.