Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR

The random forest algorithm and whale optimization algorithm were introduced in the construction of the prediction model of concrete dam deformation based on WOA-RFR to improve the prediction accuracy and model performance. The random forest model belonging to the machine learning algorithm has many advantages such as strong generalization ability and fast training speed, and it has a strong mapping capability for nonlinear features. However, because different parameters and corresponding parameter combinations of the primitive random forest algorithm have a great influence on the improvement and stability of the model performance, the effectiveness of the results cannot be guaranteed under the manual empirical method. Therefore, to address the parameter calibration of the random forest model, the whale optimization algorithm with strong global search ability is introduced to conduct combination optimization on key parameters. The aim is to further enhance the model's generalization ability and robustness at