Traditional single-model prediction methods suffer from issues like low accuracy, susceptibility to noise, and limited generalization capability. To address these challenges, we propose a novel approach for predicting concrete dam deformation by integrating the Beta Prior Principal Component Analysis (BP-PCA) and the Water Cycle Algorithm (WCA). Initially, the BP-PCA model decomposes deformation data into multiple scales, effectively reducing noise. This decomposition transforms the intricate nonlinear and non-stationary stochastic process into a set of principal components with simplified structures. Simultaneously, it enhances noise robustness by suppressing noise during the decomposition process. Subsequently, we employ the Water Cycle Algorithm optimized Support Vector Machine (WCA-SVM) to construct prediction models for each principal component. Finally, we integrate the prediction outcomes from multiple principal components to derive the final prediction result. The relative prediction error is minimized to 1.07%, with a root mean square error of 0.065. Compared to the three methods included in the comparative analysis, our approach yields over 62% improvement in prediction performance, demonstrating superior noise robustness and generalization capability.