Table 1.
Common rockburst prediction models.
| Construction method | Model source | Accuracy rate/% |
|---|---|---|
| C5.0 decision tree | Ghasemi et al.11 | 89.29 |
| extreme learning machine | Li et al.12 and Xue et al.13 | 80.87 |
| Probabilistic neural networks | Ma et al.16 | 90 |
| Random forest models | Xu et al.17 | 90 |
| DLNN/SVM | Saha et al.20 | 91.44 |
| Single integrated tree | Li et al.26 | 85.71 |
| XGBoost/ET/RF | Li et al.26 | 88.89 |
| sandcat swarm optimisation /extreme gradient boosting | Qiu et al.27 | 88.46 |
| RF/GBDT/XGB/PCC | Zhang et al.30 | 75.08 |
| AdaBoost | Ma Ke31 | 93.8 |
| t-SNE/ K-means clustering/XGBoost | Ullah Barkat et al. 32 | 89 |
| APSO/SVM | Li Yuefeng et al.33 | 95 |
| C4.5 decision tree algorithm | Wang Yanbin et al.34 | 71.43 |