## 3 Case Study
### Screening the Main Factor Set with Feature Selection Method  
Following the determination of the HT\({}_{\rm H}\)T factor set, the importance of the features in the factor set needs to be analyzed. During the construction of the model, the input features have a substantial impact on the performance of the model. Deformation prediction models map complex non-linear relationships between deformations and features. The introduction of redundant features into the model can potentially result in overfitting. Conversely, an insufficient number of features may result in the model disregarding potential relationships between the data, leading to underfitting. In this paper, the Relieff feature selection method is employed to rank the importance of input features in the factor set for feature screening. The feature importance ranking of different measurement points is calculated by the Relieff feature selection method, and it is evident that there is a consistent trend in the feature importance ranking of measurement points in the same area. The results are shown in Figure 11. It can be assumed that the temperature factor exerts a greater influence on dam displacement in the non-overflow dam section, while the hydraulic pressure factor has a greater effect on dam displacement in the overflow dam section. Furthermore, the percentage of characteristic importance of neighboring points is found to be similar. At the same elevation, the points of neighboring dam sections demonstrate a high degree of consistency in the percentage of characteristic importance. In different elevations, the close vertical distance between two measurement points of different elevations in the studied example leads to similar characteristics of the two measurement points. This results in the percentage of characteristic importance of the two measurement points being similar, which is in line with the conclusions of the reference [58]. According to the order of feature importance derived for different measurement points from the largest to the smallest, the features are sequentially input into the PSO-LSSVM model for training and testing. The input factor set with the smallest RMSE value is selected as the optimal factor set after feature filtering. The results of the analyses for each measurement point are presented in Figure 12. Following a thorough analysis, it can be concluded that, when fewer factors are employed, the RMSE is comparatively large. This is  
Figure 10: The fitting curve graph for the comparison of model prediction performance. due to the fact that the input factors of the model do not contain sufficient information to accurately reflect the nonlinear characteristics of the dam deformation. Conversely, when all factors with high feature importance are used as input to the model, the RMSE of the model decreases significantly and reaches its lowest point. If the number of factors is further increased, the RMSE of the testing and training sets will tend to increase continuously. This is because the redundant information should have a negative effect on the performance of the model. Therefore, based on the results presented in Figure 12, the optimal sets of feature factors for different measurement points are selected, as illustrated in Table 3.  
Figure 11: _Cont._  
Figure 11: Importance weights of features at different measurement points. Take four measurement points (YZ16-1, YZ21-1, YZ47-1, and DC23-1) to plot the displacement prediction fitting curve graph; the prediction value before and after the feature selection is compared with the real value, and the analysis results are shown in Figure 13 and Table 4. It has been determined through analysis that the prediction accuracy is further enhanced following the implementation of feature selection. Compared with the factor sets without feature selection, the average enhancement rate of each measurement point in R\({}^{2}\), MAE, and RMSE has reached 0.13%, 7.52%, and 6.97%, respectively.