## 4 Analysis of Attitude-Prediction Results
### Prediction Model Validation  
Figure 8 shows the relationship curves between the four selected attitude trajectory parameters and the monitoring date. As shown in Figure 8, the data of the four attitude deviation parameters significantly changed, and the data of the FVD and BVD parameters abruptly changed on 23 April 2022. This was because the selected parameter of the shield section through the strata was variable. Furthermore, there were different slope or curve sections of the tunnel, and the problem of tunnel segment floating caused by the improper control of the grouting pressure or slurry mix ratio occurred on site. The PCA-SVR model divided the parameter dataset into 80% training and 20% test sets. Considering that the original monitoring dataset sampling was automatically ordered, the attitude parameter data had abrupt sections with a large degree of change over time (Figure 8). This could lead to a poor training effect in the verification or test set, thereby deteriorating the overall performance of the prediction model. Thus, this study randomly shuffled the dataset, which solved the problem of abrupt sections and a large degree of change in the attitude parameter data. In addition, a randomly divided dataset may be more representative of the entire data (including uncollected data). Figure 9 presents the correlation prediction and monitoring results for the four attitude trajectory parameters in the test set. The hybrid model predicted ranges of \(-\)36 to 36 m for FHD, \(-\)11 to 32 mm for FVD, \(-\)68 to 43 mm for BHD, and \(-\)58 to 60 mm for BVD, all of which fell within the corresponding monitoring data ranges. Furthermore, the model's performance was evaluated with \(R^{2}\) values of 0.938, 0.917, 0.929, and 0.950, along with RMSE values of 3.79, 5.88, 5.53, and 4.61, and MAE values of 2.68, 1.73, 3.36, and 3.22, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Index** & **FHD** & **FVD** & **BHD** & **BVD** \\ \hline \multirow{3}{*}{PCA–SVR} & \(R^{2}\) & 0.938 & 0.917 & 0.929 & 0.950 \\  & RMSE & 3.79 & 5.88 & 5.53 & 4.61 \\  & MAE & 2.68 & 1.73 & 3.36 & 3.22 \\ \hline \multirow{3}{*}{SVR} & \(R^{2}\) & 0.956 & 0.95 & 0.957 & 0.974 \\  & RMSE & 3.17 & 3.47 & 4.52 & 3.36 \\ \cline{1-1}  & MAE & 2.22 & 1.36 & 3.18 & 2.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Absolute prediction accuracy of different attitude parameters. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Principal Component** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline Contribution rate & 0.23 & 0.16 & 0.13 & 0.10 & 0.06 & 0.05 & 0.04 & 0.03 & 0.03 & 0.02 & 0.02 & 0.02 & 0.01 \\ Cumulative contribution rate & 0.23 & 0.39 & 0.52 & 0.62 & 0.68 & 0.73 & 0.77 & 0.80 & 0.83 & 0.85 & 0.87 & 0.89 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Principal component contribution of input parameters. Figure 8: The relationship between attitude parameters and monitoring date. (**a**) FHD; (**b**) FVD; (**c**) BHD; (**d**) BVD. Figure 9: Prediction and monitoring results of attitude parameters. (**a**) FHD; (**b**) FVD; (**c**) BHD; (**d**) BVD. The coefficients of determination, \(R^{2}\), for the attitude parameter prediction of the FHD, FVD, BHD, and BVD were 0.938, 0.917, 0.929, and 0.950, respectively. They all exceeded 0.9, and other error evaluation indexes were small, indicating that the attitude parameter prediction effect was good (Figure 9). An engineering example confirmed that the proposed model and method based on machine learning were reliable and could accurately predict the trajectory of attitude motion during shield tunneling. The Pearson correlation coefficient (\(P\) value) was employed to study the sensitivity and analyze the importance of input parameters in the posture correction process.