## 6 Conclusions  
In the present study, for the rockburst intensity level prediction problem, eight machine learning classification prediction models were established to predict the rockburst intensity level, based on six evaluation indexes, and compared and analysed with other machine learning prediction models. Furthermore, the study also utilised the SHAP method to conduct a thorough analysis of the models to explain the affect or importance of each feature on the prediction of the rockburst intensity level, and the final conclusions were obtained as follows. The study collected 287 sets of real rockburst data from different regions, geological conditions and times to constitute a sample library for the machine learning prediction model, which included six characteristic parameters, such as the maximum tangential stress \(\sigma_{\theta}\)(MPa), uniaxial compressive strength of rock \(\sigma_{c}\)(MPa), the tensile strength of rock \(\sigma_{t}\)(MPa), stress ratio \(\sigma_{\theta}/\sigma_{c}\), britteness ratio of rock \(\sigma_{c}/\sigma_{t}\) and elastic strain energy index \(W_{\text{et}}\), and it analysed the correlation between the different evaluation indexes using the Pearson correlation coefficient method. The study constructed eight machine learning classification prediction models based on machine learning techniques and evaluated the prediction performance of each model using calculations and found that the NGO-CNN-BiGRU-Attention model achieved an accuracy of 0.98, which is the highest among all models tested, and the other models' accuracies were, in order, 0.96 for CNN-BiGRU-Attention,0.94 for RF, 0.93 for BiGRU, 0.89 for ELM, 0.86 for CNN, 0.82 for KNN, and 0.79 for SVM. Since the NGO algorithm enables the model to better capture the global information, although the CNN-BiGRU-Attention rockburst prediction model shows good prediction performance, there is a certain level of accuracy compared with the NGO-CNN-BiGRU-Attention model; this reflects the good automatic optimisation and robustness of the NGO algorithm and also shows that the NGO-CNN-BiGRU-Attention model has good performance in the area of rockburst intensity level prediction, which can provide some guidance for related research. For the prediction results of different models, the SHAP method was employed to quantify the impact of different evaluation indexes on the intensity level of rockbursts, and the results show that the elastic strain energy index \(W_{et}\) and stress ratio \(\sigma_{\theta}/\sigma_{c}\) have the greatest influence on the intensity level of rockbursts, and their characteristic importance is as high as 0.21 and 0.201, respectively. For the other characteristics, although their importance is not as high, they have a non-negligible influence on the intensity level of rockbursts. The other features are not as important, but their influence on the intensity level of the rockburst is not negligible. Therefore, these features should be taken into account in \begin{table}
\begin{tabular}{c c} \hline
**Model** & **Accuracy** \\ \hline NGO-CNN-BiGRU-Attention & 1 \\ CNN-BiGRU-Attention & 1 \\ RF & 0.875 \\ BiGRU & 0.875 \\ ELM & 0.875 \\ CNN & 0.75 \\ KNN & 0.75 \\ SVM & 0.625 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of the accuracy of the models. engineering applications and research to make the prediction of rockburst intensity levels more reliable. The NGO-CNN-BiGRU-Attention model is applied to eight sets of real rockburst cases in the Daxialing Tunnel, and the findings indicate that the model has excellent prediction performance, strong generalisation ability and good engineering practicability, which can be applied to the prediction of the rockburst intensity level in real engineering. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14135719/s1](https://www.mdpi.com/article/10.3390/app14135719/s1)]([https://www.mdpi.com/article/10.3390/app14135719/s1](https://www.mdpi.com/article/10.3390/app14135719/s1)), Database of rockburst samples.