## 5 Comparative Analysis of Forecast Results  
### Model Parameter Presets  
The research process processed 1-680 tunnel boring machine (TBM) shield tunneling parameters. Due to the enormous volume of raw data and variations in data quality, which could impact the model's predictive performance, samples were selected from different tunneling modes. For the TBM mode, the training set comprised rings 120 to 138, with the prediction segment spanning rings 139 to 146. Under the Earth Pressure Balance (EPB) mode, the training set included rings 435 to 444, and the prediction segment covered rings 445 to 450. Approximately 310,000 data points for each parameter--tunneling speed, total thrust, cutterhead torque, tunneling pressure, and cutterhead speed--were selected, totaling around 1.55 million data points. The constructed model is a real-time prediction model with a rectified linear unit (ReLU) as the activation function. The time step was set to 20, utilizing the past 20 time steps of data as input to predict future data. The number of epochs was set to 200. Considering that optimization algorithms can optimize multiple hyperparameters in the LSTM model, such as the number of LSTM layers, the number of LSTM units in hidden layers, learning rate, batch size, etc. Given the limitations of existing computer performance, it is advisable to choose critical hyperparameters. In line with previous research experience, this study selected two commonly optimized hyperparameters before including optimization algorithms: the number of LSTM hidden layer neurons and the learning rate. Prior to incorporating optimization algorithms, preliminary tuning was conducted for other hyperparameters. The pre-training hyperparameter settings are presented in Table 3, with 30 combinations tested during the preliminary tuning, and the relative errors are compared in Figure 7.  
Figure 7: Comparison of relative errors in pre-training.  
After multiple pre-tuning experiments, the minimum average percentage error was 14.4%. Regarding the model's accuracy, using the z-score normalization method for data processing, setting the number of LSTM layers to 2, and configuring the batch size to 256 were more effective in training the model. Considering the dimensional differences in the model's input data, which could influence the model's output, the z-score normalization method was employed to transform data with different dimensions into a unified scale. This ensures relative balance in the importance of these data during model computations, mitigating interference caused by dimensional disparities. The network structure becomes more complex when the number of LSTM layers is excessive. Although increasing the number of layers can enhance model performance and help capture deeper features and relationships, this improvement is dynamic. With the increased model complexity, the training process may face the risk of gradient explosions.  
Additionally, computational resources and training time would also increase. After a series of pre-training experiments, it was determined that a two-layer LSTM configuration is optimal for the model. Furthermore, setting the batch size to 256 improves the model's convergence speed and generalization ability. A larger batch size can simultaneously process more data, reducing noise and fluctuations. During training, using a batch size that is too small may lead to overfitting of the model to the training data, resulting in inadequate generalization of new data.