Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR - FENG Yu; WU Yunxing; GU Wenjing; PANG Qiong; GU Yanchang; CHEN Siyu (Renmin Zhujiang)
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 the same time as obtaining optimal parameter combinations. The monitoring model of dam deformation is built by using the random forest optimized by whale algorithm for an actual project, and the coefficient of determination, root mean square error (RMSE), and mean absolute percentage error (MAPE) are introduced to evaluate and compare the excellent performance of the proposed models. The prediction results were compared with different intelligent optimization algorithms and multiple control models. The results show that the WOA-RFR prediction model has higher prediction accuracy and stability, and WOA optimization significantly improves the model performance.