A large number of pumped storage power stations have been planned and constructed worldwide in recent years. Considering the rapid rate and large amplitude of reservoir water level fluctuations, an improved deformation monitoring model adapted to the operational characteristics of pumped storage dams is proposed. Firstly, the influence factors in the classical deformation prediction model are improved by introducing the water-level change rate term. To reduce the adverse effect of multicollinearity among influence factors on prediction accuracy, kernel principal component analysis (KPCA) is adopted to optimize the factor combination for the monitoring model. Secondly, to overcome the limitation of deterministic prediction in existing dam safety monitoring models, deep learning and interval prediction are integrated. This study proposes an improved deformation influence factor model considering water-level change rate and establishes a deterministic–interval joint prediction framework for pumped storage dam deformation. A case study shows that the MAE of the proposed model is reduced by an average of 24.98% relative to the compared model. The proposed fusion model delivers high-precision deterministic predictions of dam deformation and generates corresponding prediction intervals to quantify predictive uncertainty. It can provide more comprehensive support for the safety monitoring and evaluation of pumped storage dams.
Song et al. (Mon,) studied this question.