Missing value processing is an important foundation for analysis of dam safety monitoring data. Traditional methods for handling the missing values of a dam often use a single type of machine learning models for prediction and interpolation

ineffective in integrating the advantages of multiple types of machine learning models. This article integrates multiple classic machine learning and deep learning algorithms into a strong learner within the framework of ensemble learning. To address the issue of weight allocation to each model

we develop a new critic stacking (CS) weight allocation method so that we can construct a dam monitoring data interpolation hybrid model based on CS ensemble learning. The results show that compared to single base learners and traditional Stacking ensemble models

this CRITIC-Stacking ensemble learning method reduces the RMSE index by an average of 72.7% and 58%. This indicates that the method can fully leverage the predictive advantages of various machine learning models

and the improvement of weight allocation can also improve the predictive accuracy of ensemble learning models

thus providing a new solution for handling missing values in dam monitoring data and constructing prediction models.