Forecasting Dam Displacements with Limited Monitoring Data via Sequential Statistical–Deep Regression Modelling
Yu Wu, Hesam Kiarad, M. Hassani, Hassan Sarmadi, Alireza Entezami
Dam displacement monitoring is imperative to assess the operational status and structural safety of dams under various environmental conditions and operational loads. Although most of the dam structures are instrumented with robust in-situ sensing systems, long-term field monitoring is often constrained by practical challenges such as instrumentation costs, sensor drift or malfunction, data gaps, and the inherent complexity of operating and maintaining large-scale monitoring networks. Machine learning offers an alternative solution to deal with these challenges by enabling data-driven prediction and interpretation of structural responses. Despite numerous regression-based predictive models for predicting dam structural responses, a demanding issue arises from incomplete training data stemming from the unavailability of some influential environmental and operational factors. This study aims to address the aforementioned engineering and technical limitations by proposing an intelligent hybrid regressor. This model integrates ridge regression with a convolutional neural network (CNN), leveraging the strengths of both statistical learning and deep learning paradigms, centralized on a residual correction mechanism. First, the ridge regression model performs initial dam displacement predictions using the available environmental and operational factors. Second, prediction errors (residuals) of the ridge regression model are fed into the CNN to capture hidden nonlinear relationships embedded in the residuals and subsequently enhance overall prediction accuracy. Given this sequential dual-stage prediction architecture, the proposed hybrid regressor can address the limitation of unavailable environmental and operational factors that significantly influence dam deformation behaviour. A real-world dam structure, along with limited data including reservoir levels and temperature, is employed to validate the proposed predictive method. Results show that the proposed hybrid regressor achieves a prediction accuracy of approximately 89% using incomplete data while effectively capturing strong nonlinear characteristics in dam displacement responses.