## 5 Comparative Analysis of Forecast Results
### Exacavation Rate Prediction Analysis  
The four optimization algorithms set the optimization range for the number of neurons in the LSTM hidden layer between 10 and 100, and the optimization range for the learning rate is set between 0.001 and 0.1. At the same time, other parameters use the model's default values. Figure 8 illustrates the predictive results of the LSTM model on the test set under different optimization algorithms. In regression models, different evaluation metrics focus on different aspects. Evaluating with a single metric may overlook other important factors, so a comprehensive evaluation method using multiple metrics is necessary. [PERSON] et al. (2019) proposed the ranking method as a commonly used multi-metric comprehensive evaluation method in 2008. This method involves ranking \(N\) models under the same evaluation metric, \(i\). Then, the rankings of \(m\) different evaluation metrics for the same model are summed to obtain the multi-metric ranking for that model, as shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Predictive**} & \multicolumn{2}{c}{**Optimization Results**} & \multirow{2}{*}{**Training**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAPE**} & \multirow{2}{*}{**Overall**} & \multirow{2}{*}{**Rank**} \\ \cline{2-2} \cline{5-10}  & **Units** & & & & & & & & & & \\ \hline BO-LSTM & 32 & 0.0094 & 367(1) & 1.63(2) & 3.51(1) & 2.17(2) & 4.49(1) & 8.0\%(1) & 13.8\%(1) & 9 & 1 \\ DE-LSTM & 36 & 0.0085 & 800(3) & 1.62(1) & 3.93(3) & 2.16(1) & 4.88(3) & 8.0\%(1) & 15.9\%(3) & 15 & 2 \\ GA-LSTM & 23 & 0.0086 & 1011(4) & 1.82(4) & 4.22(2) & 2.38(4) & 5.21(4) & 9.3\%(3) & 16.9\%(4) & 27 & 4 \\ PSO-LSTM & 19 & 0.0150 & 396(2) & 1.74(3) & 3.77(2) & 2.24(3) & 4.83(2) & 8.7\%(2) & 14.6\%(2) & 16 & 3 \\ \hline \hline \end{tabular} Note: ( ) is a single-column ranking.  
\end{table} 
Table 4: Prediction effect ranking analysis. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Optimize Parameter Range** \\ \hline Normalization method & max-min normalization, z-score normalization \\ \hline Batch size & 16, 32, 64, 128, 256 \\ \hline Number of LSTM layers & 1, 2, 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pre-training setup. After comparing the performance of different optimization models on the test set, the conclusion can be drawn that, regarding the optimization of model hyperparameters, the number of neurons in the LSTM hidden layer or the learning rate should be manageable. Many neurons in the LSTM hidden layer can lead to an overly complex model, making it prone to overfitting. Conversely, more neurons are needed to ensure the model captures complex patterns and relationships, resulting in an insufficient utilization of information from the input data. A learning rate that is too small may slow down network training, requiring a longer time to converge, while a learning rate that is too large may lead to unstable training, skipping the optimal points and preventing convergence. Therefore, it is necessary to use hyperparameter optimization algorithms for multiple training iterations to achieve optimal predictive performance. Examining the prediction curves for specific tunneling ring numbers reveals that the trends in the predicted data under both tunnel boring machine (TBM) and earth pressure balance (EPB) shield modes generally align with the actual excavation parameter curves. Under the TBM mode, the predicted curve for the weathered zone aligns well with the actual values, demonstrating overall good predictive performance. However, there is significant fluctuation in the local prediction segment from ring 139 to ring 141 due to unfavorable geological conditions characterized by complex rock changes in a poor geological area. This section underwent pre-reinforcement during construction, contributing to relatively poorer predictive results than other segments.