# A Decision Tree for Rockburst Conditions Prediction
## 5 Conclusions  
Predicting the rockburst condition plays a vital role in the safety, economy, performance, and efficiency of deep underground projects. In this research, a Decision Tree (DT), which is a simple, efficient, and accurate technique, was utilized to predict rockburst conditions for different rock types, such as igneous, metamorphic, and sedimentary, both alone and together. A new model was developed by combining datasets for each rock type and modelling it, using the DT algorithm, to predict rockburst conditions. Other Machine Learning (ML) algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and AdaboostM1 were also utilized in predicting rockburst condition. Training and testing of the models were performed on a representative dataset of 210 records containing 5 input variables, required for forecasting rockburst conditions. The dataset contains 103 igneous rockburst cases, 58 metamorphic, and 49 sedimentary.  
The approach, using different DT models for each rock type, is very restrictive, in comparison with the Unique-DT model, which has a wider domain of application. Among the DT models, the one related to metamorphic rocks provided the best results. However, it can only be applied to one type of rock. Furthermore, based on the evaluated metrics, the Unique-DT (F1 = 0.65) algorithm showed a very promising performance. Although other ML algorithms were utilized and compared to Unique-DT, RF (F1 = 0.66) and AdaboostM1 (F1 = 0.66) were slightly better in performance metrics. Taking into account its simplicity and effectiveness, the Unique-DT model is suggested to be used in predicting rockburst conditions.  
Subsequently, more focus should be placed on ensemble methods, such as RF and boosting algorithms, such as AdaboostM1, as they have demonstrated strong performance in classification tasks. Moreover, to improve prediction accuracy, authors intend to incorporate additional data--rockburst cases and results obtained from different input variables and ML methods in future work.  
In summary, the ML algorithms have shown that they can be used for predicting rockburst conditions if data are available. Also, the use of DT in rockburst prediction based on depth, elastic energy index, and strength rock parameters has proved effective, and demonstrated its merit in solving this complex phenomenon--rockburst.  
Conceptualization, J.T., D.O.-A. and F.L.; Software, J.T. and D.O.-A.; Investigation, D.O.-A., J.T. and F.L.; Methodology, D.O.-A., J.T. and F.L.; Validation, D.O.-A., J.T., F.L., F.M. and J.M.; Writing--original draft, D.O.-A.; Writing--review and editing, D.O.-A., J.T., F.M. and F.L.; Project administration, J.T.; Funding acquisition, J.T. and J.M. All authors have read and agreed to the published version of the manuscript.  
This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020.  
Not applicable.  
The data used in the present study are under privacy issues and cannot be shared.  
The authors declare no conflict of interest.