During the operation of a dam

its original monitoring data exhibit complex

diverse

and time-varying characteristics

leading to gradual reduction in the effectiveness and accuracy of long-term monitoring warnings and thereby increasing disaster risks. Therefore

developing efficient and accurate deformation monitoring models is crucial to dam safety assessment. Traditional deterministic point predictions of a dam system

due to its inherent uncertainty

are faced with unavoidable challenges in error

bringing in low accuracy and a difficulty in determining the main factors of dam deformation. This paper presents a novel method that combines eXtreme Gradient Boosting with Bootstrap to construct prediction intervals. We use Elastic Net to extract the features of displacement influencing factors

and Bayesian Optimization to search for its optimal parameters. It can effectively estimate its own bias by combining multiple XGBoost models through Bootstrap; through residual training of the ensemble model

it further estimates the variance of random noise

quantifying the uncertainty of dam deformation. We validate this method in engineering case studies against the monitoring data from the Baihetan extra high arch dam under operation. Comparison of its predictions with the measurements and those predicted using a single model verifies its high accuracy and robustness

showing its root mean square error of only 0.0112. The accuracy of the model reaches 96%

and the efficiency is raised by up to 71% compared with the single model.