# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 4 Results and Discussion
### Discussion  
In order to provide further validation of the superiority of the TRNet model in comparison to other models, a comparison of the prediction performance of several common \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Search Area** & **Optimal Hyperparameters** \\ \hline The dimension of hidden layer & 32, 64, 128, 512 & 64 \\ The dimension of KAN & 16, 32, 64, 128, 512 & [64, 32, 16] \\ Batch size & 16, 32, 64, 128, 256 & 32 \\ Dropout & 0.01, 0.05, 0.1, 0.2 & 0.05 \\ Patience & 5, 10, 20 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimal hyperparameters and search ranges. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial** & **Accuracy** & **AUC** & **Kappa** & **Epoch** & **Train Duration/s** \\ \hline
1 & 0.92029 & 0.956131 & 0.741277 & 12 & 5.960255 \\
2 & 0.92029 & 0.944145 & 0.741277 & 11 & 5.961756 \\
3 & 0.921739 & 0.959131 & 0.746631 & 13 & 5.96497 \\
4 & 0.  
machine learning models and deep learning models was conducted on the test set. The models included in the comparison were XGBoost, RF, LSTM, CNN and KAN. It was ensured that a uniform training strategy and architecture were used for all models, and the hyperparameters of the models were optimised by the Bayesian optimisation algorithm. The results, as presented in Table 8, demonstrate that TRNet consistently achieves the highest prediction accuracy across all three evaluation metrics when compared to the other models. With regard to classification accuracy, TRNet attains 0.9214, representing an enhancement of 0.98%, 0.03%, and 1.84% over CNN, LSTM, and KAN, respectively. In terms of AUC metrics, TRNet exhibits a substantial superiority over the comparison models with 0.9590, marking an advancement of 1.67 percentage points over the nearest CNN, and a notable improvement of 7. The results demonstrate that TRNet enhances the Kappa coefficient, which is a metric of classification consistency, by 4.13% and 2.49% in comparison to CNN and KAN, respectively. This suggests that TRNet can enhance the prediction accuracy of multi-source data. This may be due to the fact that TRNet can improve the ability to perceive the intrinsic patterns of multi-source data as well as the ability to explain them by identifying the causal relationships between them, which in turn better improves the prediction effect. It is noteworthy that, among the deep learning models, CNN and TRNet exhibit the most analogous prediction accuracy. However, they necessitate more than twice the number of training rounds from the outset of training to fitting, and the aggregate training time is considerably more extensive than that of TRNet, although the training time of each round is marginally lower than that of TRNet. In contrast, the two common machine learning models, KNN and XGBoost, do not necessitate a substantial number of training rounds and exhibit the shortest training time. However, their prediction accuracies are lower compared to the deep learning models, which may be due to the difficulty of traditional machine learning models in coping with noisy data, and their weak ability to perceive the intrinsic patterns of multimodal data due to the lack of mapping relationships in high-dimensional hidden layers. To facilitate a more visual comparison of the model predictions, Figure 14 plots the scores of various evaluation metrics. As shown in Figure 14a, TRNet and LSTM exhibit higher accuracy and Kappa scores, while the ROC curves in Figure 14b demonstrate TRNet's superior prediction capabilities. The confusion matrix in Figure 15 reveals discrepancies in classification outcomes, particularly for Class III and Class IV perimeter rocks. Most misclassifications (excluding XGBoost) involve labelling Class IV rocks as Class III, highlighting variations in model tendencies. Detailed analysis shows that TRNet not only achieves the highest overall accuracy but also maintains a balanced performance across rock classes.