Abstract: Deformation of channel protection dikes is influenced by numerous uncertainties, making it difficult for a single mathematical model to fully exploit and utilize the valuable information contained in monitoring data. This limitation results in low prediction accuracy and high false alarm rates in monitoring efforts. To address these challenges, this study applies the sequential Monte Carlo (SMC) approximate Bayesian method to estimate parameter probability distributions and calculate the posterior probabilities of eight commonly used hydraulic structure deformation monitoring models. The posterior probabilities are then employed as fusion weights to construct an ensemble monitoring model that maximizes the advantages of each individual model. A case study on a concrete protection dike in a hydropower project demonstrates that the multi-model fusion online monitoring model established by this method provides a more accurate description of deformation patterns and trends. Compared to individual models, it significantly improves prediction accuracy and reduces false alarm rates. By reasonably integrating different mathematical models through probabilistic weighting, the multi-model fusion monitoring technique effectively compensates for the shortcomings of individual models, reduces uncertainties in deformation monitoring, enhances the accuracy of safety warnings, and offers new ideas and methods for monitoring the deformation of channel protection dikes.