## 3 Case Study
### Analysis Results  
#### 3.3.1 Construction of SFEMM Model  
The findings from a series of 400 simulations employing the SFEM were subjected to rigorous analysis. The mean horizontal displacement at the B3 monitoring point was assessed under varying upstream water depths (the calculated water level minus the standard dead level). These data were then modeled through curve fittings, employing Equation (7), \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Zones**} & \multicolumn{3}{c}{\(\varphi_{0}\) (\({}^{\circ}\))} & \multicolumn{3}{c}{\(n\)} & \multicolumn{3}{c}{\(K_{b}\)} \\ \cline{2-10}  & \(\mu\) & \(\sigma\) & \(C.V\) & \(\mu\) & \(\sigma\) & \(C.V\) & \(\mu\) & \(\sigma\) & \(C.V\) \\ \hline Upstream rockfill & 50.3 & 5.03 & 0.1 & 0.91 & 0.091 & 0.1 & 383 & 38.3 & 0.1 \\ Main rockfill & 50.5 & 5.05 & 0.1 & 0.853 & 0.0853 & 0.1 & 455 & 45.5 & 0.1 \\ Downstream rockfill & 47.5 & 9.5 & 0.2 & 0.92 & 0.184 & 0.2 & 225 & 45 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Eigenvalues of the parameters random fields. Figure 7: Stability of SREM results. in order to ascertain the water pressure component expression. The efficacy of this approach is visualized in Figure 8, and the resulting fit can be expressed in Equation (12). \[\delta_{H} =9\times 10^{-5}H^{3}+6\times 10^{-4}H^{2}+9.07\times 10^{-2}H+2.8544 \tag{12}\]  
Accordingly, the SFEMM model, taking into account the uncertainty of the rockfill, can be constructed:  
\[ \begin{array}{l}y=L\delta_{H}+\delta_{T}+\delta_{\theta}\\ =L\big{(}9\times 10^{-5}H^{3}+6\times 10^{-4}H^{2}+9.07\times 10^{-2}H+2.8544 \big{)}\\ +\sum\limits_{i=1}^{m_{1}}b_{1}T_{i}+\sum\limits_{i=1}^{m_{2}}\left(b_{1i}\cos \frac{2\pi iti}{365}+b_{2i}\sin\frac{2\pi iti}{365}\right)+c_{1}\big{(}1-e^{-c_ {2}\theta}\big{)}\end{array}  \tag{13}\]  
The established SFEMM model is utilized for the analysis and prediction of the measured horizontal displacement at monitoring point B3. A total of 1660 sets of monitoring data are employed for model training, and the final 100 sets are used for prediction analysis to evaluate the model's accuracy. Furthermore, three additional monitoring models are selected for comparative prediction analysis to ascertain the superiority of the SFEMM model. These are the least squares regression monitoring model (LSRM model), the support vector machine monitoring model (SVRM model), and the conventional finite element method monitoring model (FEMM model). The results of this analysis are shown in Figure 9. (1) The prediction accuracy of each model for the first 28 sets of data is maintained at a high level, but with an increase in the number of prediction samples, the prediction error of each model shows a tendency to increase, and the prediction accuracy gradually decreases. (2) When the prediction data exceed 46 sets, the SVRM model is overfitted and exhibits poor stability, and its predicted values show the phenomenon of \"oscillation\". (3) The prediction error of the LSRM model is obviously larger after 28 sets of data, and the prediction accuracy is lower. (4) It is evident that, while there is a strong correlation between prediction and measured values for both the FEMM and SFEMM models, the SFEMM model does in fact demonstrate a higher level of accuracy. Additionally, the predictive performance of each model is evaluated using five indexes, as shown in Figure 10: (1) The SFEMM model has the smallest prediction errors among the four models, in which the RMSRE is less than 4.7%, and the MRE is less than 3.8%, whereas the other four models have the RMSRE greater than 6.3% cm, and the MRE is higher than 4.9%. (2) The correlation coefficients between the predicted and measured values from the SFEMM and LSRM models are all greater than 0.96, which is higher than those of the other monitoring models. (3) The distribution characteristics of the predicted values of the  
Figure 8: Fitting curve of water pressure component.