## 5 Discussion
### Examination of the Developed Models  
The results obtained in this study demonstrate the effectiveness of the proposed PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models in addressing the challenges in rockburst hazard prediction. These models achieved significant improvements in classification accuracy, particularly for underrepresented classes, highlighting the importance of integrating optimization techniques and ensemble learning algorithms. Compared to other models, the developed PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models exhibited the highest accuracy. By further examining their characteristics, some challenges were identified, and corresponding solutions were suggested. Although PSO and BP algorithms are commonly used for parameter adjustment, their individual flaws can sometimes lead to poor accuracy. Specifically, the complexity of particle swarm behavior may hinder certain model steps, while BP algorithms may suffer from issues such as gradient vanishing, gradient explosion, and inappropriate learning rates. By combining the advantages of both algorithms, the PSO-BPNN algorithm is better equipped to achieve optimization and reduce unpredictability than single algorithms. As powerful optimization methods, AdaBoost and XGBoost can enhance the global performance of the integrated model by improving the training effects of base learners. They also reduce the risk of overfitting and improve model generalization through regularization. AdaBoost works by assigning different weights to samples based on their error rates, enabling effective addressing of high-error samples. This process leads to improved global accuracy of the integrated model through weighted voting among base learners. However, AdaBoost is known to be sensitive to outliers, which can significantly impact training when present in large numbers. To address this issue, preprocessing techniques can be employed to detect and remove outliers using local outlier factors. Alternatively, incorporating a weighting mechanism to reduce the influence of outliers or adopting alternative loss functions, such as the Huber function, can effectively manage small outliers. On the other hand, XGBoost optimizes base learners using regularization and gradient-boosting techniques. Overfitting can be mitigated by limiting tree depth and adding randomness to the model. Fine-tuning learning rates and sample weights through grid search and five-fold cross-validation further enhances model performance. The superior performance of the PSO-BPNN-AdaBoost and PSO-BPNN-XGBoost models aligns with the findings of [PERSON] et al. [69], which demonstrated the effectiveness of XGBoost in predicting rockburst hazards in complex environments. Our study extends their work by incorporating the PSO algorithm for enhanced optimization, resulting in higher accuracy and robustness. Similarly, the findings of [PERSON] et al. [40], which emphasizedthe strength of ensemble learning methods in handling imbalanced data, are consistent with our results. The boosting techniques employed in this study proved particularly effective in improving the classification of minority classes, such as none (I) and strong (IV). The impact of SMOTE preprocessing, as observed in this study, is consistent with the conclusions of [PERSON] et al. [63], who highlighted the advantages of oversampling techniques in enhancing predictive performance for imbalanced datasets. Unlike undersampling methods, SMOTE not only balances the class distribution but also preserves the diversity of the dataset, which is critical for the generalization of machine learning models. The balanced dataset achieved through SMOTE preprocessing contributed to the improved performance of all models, particularly those sensitive to data imbalance, such as KNN and Bagging-based methods. The robustness of the proposed hybrid models under varying geological conditions is further supported by case studies involving the Zhonghanshan Tunnel, Daxiangling Tunnel, and Cangling Tunnel. These results complement the work of [PERSON] et al.