# A Decision Tree for Rockburst Conditions Prediction
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Number of Data** & **Input Variables** & **Algorithm** & **Results/Description** \\ \hline [PERSON]. [PERSON] et al. [28] & 254 & PPV, SCF, GSSC, ES, GS & stochastic gradient-boosting (SGB) & The SGB showed an average accuracy of 0.61 and a kappa of 0.43, indicating a very good performance in predicting rockburst conditions \\ \hline K. [PERSON] et al. [29] & 209 & D(m), \(\sigma_{\text{v}}\), \(\sigma_{\text{c}}\), \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\) / \(\sigma_{\text{c}}\), \(\sigma_{\text{c}}\), EEI & cloud calculation and entropy weight, KNN, BN, RF & The results obtained from the considered model showed high performance of this model compared to the other ML algorithms \\ \hline [PERSON]. [PERSON] et al. [30] & 246 & D(m), \(\sigma_{\text{v}}\), \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\) / \(\sigma_{\text{c}}\), \(\sigma_{\text{c}}\), EEI & ANN, SVM, RF, GBM, LDA, QDA, NB, KNN, CT, PLSDA & The accuracy and [PERSON]’s kappa revealed that GBM and RF performed better than the others in predicting rockburst conditions \\ \hline [PERSON] et al. [31] & 137 & D(m), \(\sigma_{\text{v}}\), \(\sigma_{\text{c}}\), \(\sigma_{\text{o}}\), EEI & BN with NB classifier & The result suggests that the error rate of the proposed BN is the lowest among traditional criteria, and it can resolve incomplete data \\ \hline [PERSON] et al. [10] & & TSUP, K, \(\sigma_{\text{c}}\), D\({}_{\text{up}}\), OMENT & BN classifiers & The results revealed high accuracy and good relationships between variables to be identified \\ \hline [PERSON] and [PERSON] [32] & 174 & \(\sigma_{\text{v}}\), \(\sigma_{\text{c}}\), \(\sigma_{\text{o}}\), EEI & BN classifiers & Overall, the results indicate that BN performs well in predicting rockburst intensity \\ \hline [PERSON] and [PERSON] [33] & 135 & D(m), \(\sigma_{\text{v}}\), \(\sigma_{\text{v}}\), \(\sigma_{\text{o}}\), EEI & Logistic Regression classifier (LRC) & The results, based on AUC and error rates, indicate that LRC is effective in predicting rockburst intensity \\ \hline [PERSON] et al. [34] & 60 & \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\)/ \(\sigma_{\text{c}}\), \(\sigma_{\text{c}}\)/ \(\sigma_{\text{v}}\), EEI & ideal-point method & The results revealed minimum error rate and a very high prediction for rockburst intensity \\ \hline [PERSON] et al. [35] & 108 & \(\sigma_{\text{v}}\), \(\sigma_{\text{c}}\), \(\sigma_{\text{o}}\), EEI & Decision Tree (DT) & The results show that moderate rockburst intensity has the best agreement with the actual circumstances \\ \hline [PERSON] and [PERSON] [36] & 134 & \(\sigma_{\text{v}}\), \(\sigma_{\text{v}}\), \(\sigma_{\text{o}}\), EEI & ENN, GEP, DT & The results showed the high accuracy and applicability of all three new models. However, the GA-ENN and the GEP methods outperformed the C4.5 method \\ \hline [PERSON] et al. [37] & 188 & D(m), \(\sigma_{\text{v}}\), \(\sigma_{\text{c}}\), \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\)/ \(\sigma_{\text{c}}\), \(\sigma_{\text{c}}\), EEI & NB, DT, SVM, ANN, KNN & The developed models show a high performance compared to the previous application of the empirical criteria \\ \hline [PERSON] et al. [38] & 246 & D(m), \(\sigma_{\text{v}}\), \(\sigma_{\text{c}}\), \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\)/ \(\sigma_{\text{c}}\), \(\sigma_{\text{c}}\), EEI & Support Vector Classifier (SVC) & Promising results in forecasting the rockburst intensity at the Kimberlite mine in Canada were achieved \\ \hline [PERSON] and [PERSON] [39] & 174 & \(\sigma_{\text{o}}\)/ \(\sigma_{\text{c}}\), \(\sigma_{\text{c}}\)/ \(\sigma_{\text{v}}\), EEI & DT & The results show the significantly high performance of the models \\ \hline [PERSON] et al. [21] & 196 & \(\sigma_{\text{v}}\), \(\sigma_{\text{c}}\), \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\)/ \(\sigma_{\text{c}}\), \(\sigma_{\text{c}}\), EEI & FA, ANN, and (FA-ANN) & The results show a significantly high performance for all three models, based on RMSE and R\({}^{2}\) \\ \hline [PERSON] et al. [24] & 102 & \(\sigma_{\text{v}}\), \(\sigma_{\text{v}}\), \(\sigma_{\text{o}}\), \(\sigma_{\text{o}}\)/ \(\sigma_{\text{v}}\), \(\sigma_{\text{c}}\), EEI & CART, Boosting, and Bagging & The results, based on accuracy, indicated that the ensemble techniques proved better for the prediction, especially the bagging \\ \hline \hline \end{tabular}  
* NB: \(\sigma_{\text{o}}\) is the maximum tangential stress of the surrounding rock, MPa, \(\sigma_{\text{c}}\) is the uniaxial compressive strength of the surrounding rock, MPa, \(\sigma_{\text{t}}\) is the tensile strength of the rock, MPa, EEI is the Elastic Energy Index, Brittle index (BI) = (\(\sigma_{\text{c}}\)/ \(\sigma_{\text{t}}\)), Stress index/Stress Concentration Factor (SI/SCF) = (\(\sigma_{\text{o}}\)/ \(\sigma_{\text{c}}\)), Peak Particle Velocity (PPV), Ground Support System Capacity (GSSC), Excavation Span (ES), Geological Structure (CS), Type of Support (TSUP), horizontal vs vertical stress ratio (K), equivalent diameter (D\({}_{\text{up}}\)), orientation of the burst in the periphery of the excavation, Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), Fuzzy Inference System (FIS), Adaptive-Neuro Fuzzy Inference System (ANFIS), K-Nearest Neighbor (KNN), Bayesian Network (BN), Naive Bayes (NB), Decision Tree (DT), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Multilinear Regression (MLR), Partial Least-squares Discriminant Analysis (PLSDA), Gradient-boosting Machine (GBM), Classification Tree (CT), Emotional Neural Network (ENN), Gene Expression Programming (GEP), Firefly Algorithm (FA).  
\end{table}