A Robust Displacement Monitoring Model for High-Arch Dams Integrating Signal Dimensionality Reduction and Deep Learning-Based Residual Correction

Abstract

Deformation is a critical indicator for the safety control of high-arch dams, yet traditional statistical regression methods often exhibit poor predictive performance when applied to long-sequence time series data. In this study, we develop a robust predictive model for deformation behavior in high-arch dams by integrating signal dimensionality reduction with deep learning (DL)-based residual correction techniques. First, the fast Fourier transform is employed to decompose air and water temperature sequences, enabling the extraction of temperature cycle characteristics at the dam boundary. A data-driven statistical monitoring model for dam deformation, based on actual temperature data, is then proposed. Subsequently, an improved Bayesian Ridge regression model is used to construct the dam deformation monitoring framework. The residuals that traditional statistical methods fail to capture are input into an enhanced Long Short-Term Memory (LSTM) network to effectively learn the temporal characteristics of the sequence. A high-arch dam with a history of long-term service is used as a case study. Experimental results indicate that the data dimensionality reduction method effectively extracts relevant information from observed temperature data, reducing the number of input variables. Comparative evaluation experiments show that the proposed hybrid predictive model outperforms existing state-of-the-art benchmark algorithms in terms of predictive efficiency and accuracy. Additionally, this approach combines the interpretability of statistical regression methods with the powerful nonlinear modeling capabilities of DL-based models, achieving a synergistic effect.