关键词: 改进小波阈值, 鹈鹕优化算法, 双向长短期神经网络, 去噪, 变形预测

Abstract: 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.

Key words: improved wavelet thresholding, pelican optimization algorithm, bidirectional long short-term memory network, denoising, deformation prediction