# A Decision Tree for Rockburst Conditions Prediction
## 3 Methods
### Multiple ML Algorithms  
The evaluation results of several ML algorithms, such as ANN, KNN, RF, SVM, AdaboostM1, and Unique-DT, were compared and presented in Table 7. RF, AdaboostM1, unique-DT, ANN, and KNN were found to perform well in predicting rockburst conditions, while SVM showed poorer performance. F1 and accuracy metrics for RF, Unique-DT, and AdaboostM1 were similar, with RF having a slight advantage. The study suggests that the Unique-DT model is a good alternative to other ML algorithms for predicting rockburst conditions. In addition, the ML algorithms used in this study are compared with some from other studies (Table 8).  
Figure 6: Unique-DT approach’s decision tree diagram.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**RF**} \\ \hline
**Rockburst Condition** & **Precision** & **Recall** & **F1** & **AUC** & **ACC** & **SP** & **MCC** \\ \hline None & 0.64 & 0.68 & 0.66 & **0.94** & 0.88 & 0.92 & 0.58 \\ Light & 0.6 & 0.53 & 0.56 & 0.85 & 0.76 & 0.85 & 0.4 \\ Moderate & 0.64 & **0.75** & 0.69 & 0.86 & 0.77 & 0.78 & 0.51 \\ Strong & **0.79** & 0.65 & **0.71** & 0.93 & **0.9** & **0.96** & **0.66** \\ \hline Average & 0.67 & 0.65 & **0.66** & 0.9 & 0.83 & 0.88 & 0.54 \\ \hline  & \multicolumn{6}{c}{**KNN**} \\ \hline None & 0.53 & 0.62 & 0.58 & 0.75 & 0.84 & 0.88 & 0.48 \\ Light & 0.52 & 0.44 & 0.47 & 0.63 & 0.71 & 0.83 & 0.28 \\ Moderate & 0.58 & 0.63 & 0.61 & 0.7 & 0.72 & 0.77 & 0.4 \\ Strong & **0.68** & **0.65** & **0.67** & **0.79** & **0.88** & **0.93** & **0.59** \\ \hline Average & 0.58 & 0.59 & 0.58 & 0.72 & 0.79 & 0.85 & 0.44 \\ \hline  & \multicolumn{6}{c}{**SVM**} \\ \hline None & 0.64 & 0.19 & 0.29 & 0.77 & 0.84 & **0.98** & 0.28 \\ Light & 0.27 & 0.39 & 0.32 & 0.52 & 0.5 & 0.55 & -0.05 \\ Moderate & 0.35 & 0.42 & 0.38 & 0.53 & 0.54 & 0.6 & 0.03 \\ Strong & **0.88** & **0.53** & **0.66** & **0.87** & **0.9** & **0.98** & **0.  
This section highlights the importance of interpretability in explaining ML algorithms, particularly sensitivity analysis of DT algorithms. The aim was to identify the relevant variables (inputs) that contribute to the prediction of rockburst conditions. Figure 7 was generated to help understand what was learned by the algorithms and compare it to empirical knowledge.  
Each rock type has distinct variables that contribute to determining rockburst conditions, with the Unique-DT model showing the most influence on elastic energy index (\(EEI=36.73\%\)). Other influential input variables include Depth (25.84\(\%\)), Brittle index (\(BI=16.13\%\)), and Stress Index (\(SI=13.85\%\)). The three DT-RT models show varying importance, based on their input variables. This is indicative of the influence the variable has on rockburst due to the rock type. The importance of the DT-RT model for sedimentary (SD) indicates EEI (43.01\(\%\)) as the most relevant factor, followed by Depth (21.19\(\%\)), BI (18.71\(\%\)), and SI (17.09\(\%\)), respectively. In the case of IG and MT, BI (27.37\(\%\)) and SI (32.21\(\%\))  
Figure 7: ML algorithms identify the most contributing variables for rockburst prediction through relative importance.  
are the most relevant factors, respectively, and EEI is the second-most important variable for both models.  
These findings align with previous studies [21; 30; 31; 33] on rockburst condition prediction, emphasizing the importance of accurately identifying underlying factors through closer examination of input data. It should be noted that all models assign high importance to the elastic energy index (EEI), which aligns with [PERSON] and [PERSON] [16] who presented a new prediction method for rockburst based on this index.