# Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
## 6 Conclusions  
This study aimed to develop tree-based predictive models for flyrock due to mine blasting. Four models, i.e., DT, RF, XGBoost, and AdaBoost, was developed to predict flyrock considering several parametric studies. These parametric studies were carried out to determine the best hyperparameters for each tree-based model. It was found that although DT and RF are among the most powerful and popular predictive models, the final results of booster algorithms (i.e., XGBoost and AdaBoost) are far better than theirs. Their robustness is because of their nature to learn from previous mistakes compared to the semi-stochastic nature of DT and RF. However, when considering results obtained by the AdaBoost and XGBoost models, the AdaBoost model with R\({}^{2}\)train = 0.99 and R\({}^{2}\)test = 0.98 showed better performance. In addition, the overall performance of each model was examined using three other performance indices (RMSE, VAF, and A10-index). As a result, the AdaBoost model's high accuracy in prediction is proved using the other indices. XGBoost used DT as a weak learner in an incremental learning process. There are similarities between these trees and DT; however, XGBoost combined the errors of the trees and attempted  
Figure 11: Feature importance analysis (**a**) AdaBoost, (**b**) XGBoost, (**c**) RF, and (**d**) DT.  
to reduce the overall error. This procedure allows XGBoost to grow the tree and learn from the previous iterations without increasing the error rate. Finally, by looking at the previously developed model using AdaBoost and XGBoost, it was concluded that AdaBoost models perform better in low-noise datasets. This technique could be chosen when it is not important whether the results are timely or computationally complex and knowledgeable users and enough time for tuning the hyperparameters are not available. However, the lack of visualization, tuning multiple hyperparameters, and sensitivity to noisy data and outliers make XGBoost and AdaBoost complex models.  
It is crucial to note that the models developed in this study are based on the blasting operations collected from one case study with limited geological conditions. Therefore, the results are only valid for further prediction if the geological conditions, blasting pattern parameters, and other important parameters are the same or very similar to those presented in this study. In addition, if inputs beyond the ranges mentioned in this research are available, the results will have larger errors. To improve performance prediction, theory-guided machine learning, which uses theories and empirical equations in the data preparation stage, can be used. These techniques may include a better understanding of the use of machine learning in mining environmental issues for a mining or civil engineer. Of course, these models can help with model generalization, which is one of the drawbacks of AI models, at least in engineering fields.  
Not applicable.  
The data is available upon request.  
The authors of this study want to thank the Sungun Copper Mine's management and mining engineers for their help in conducting this study. The authors declare no conflict of interest.