## 4 Summary and Conclusions  
This study set out with the aim of comparing the different ML techniques to predict ground vibration (PPV) due to quarry blasting. Thus, five methods, including RF, CART, CHAID, ANN, and SVM, were used. Before development of the models, an FS method was used to identify the most important input variables and reduce the dimensionality of the data. Concerning the variable importance, the results of the analysis of variable importance showed that the magnitudes of variable importance between the considered models differed. This indicates that these models identified the relationships between variables in a dissimilar manner. While RF showed a better performance for the training dataset compared to other methods used in this study, the ANN and SVM together obtained the highest rank for the testing dataset. The higher rank of RF for the training dataset shows that its flexibility, which is achieved by combining multiple decision trees, is particularly useful for predicting ground vibration. The performance of RF was significantly better than other tree-based regression models like CHAID and CART. This difference can be connected to the larger diversity among the learned trees of RF, which is a consequence of RF's procedure for randomized splitting at nodes. Typically, ensemble regression models function better if there is notable diversity among the models [99]. However, the performance of CART was inferior to both RF and CHAID. It can be explained by the fact that CART models are more prone to overfitting, while RF and CHAID conceptually aim to reduce model variance and consequently avoid overfitting. The aim of the FS techniques is not only increasing the model accuracy. The FS techniques also aim to reduce model dimensionality/complexity, training time, and overfitting risk [100]. In addition, these techniques may increase the model generalization ability [101]. It is worth mentioning that the FS techniques are heuristic, as they are just an indication of what may work well. The authors of this present study compared the predictive accuracies achieved from different models, with and without FS. It was found that the hybridization of FS and RF increased the \(R^{2}\) value, while this hybridization decreased the number of input variables. The results confirmed the effectiveness of FS technique employment before developing the RF model to predict the PPV. The evaluation using the gain chart confirmed the results of performance indices. The RF performance for the training dataset was better than that for the testing dataset. In fact, the RF model correctly predicted 100% of the samples that had a PPV of greater than 5.59 mm/s when we went through 44% of the training dataset, while, for the testing dataset, the RF model correctly predicted 100% of the samples that had a PPV of greater than 5.59 mm/s when we went through 65% of data. Better performance of RF over SVM and ANN was relatively unexpected, since both SVM and ANN use more advanced techniques compared to the RF to predict the target variable. The findings  
Figure 7: Comparison of average \(R^{2}\) values and input variable numbers of the models developed in previous and current studies. of this study suggest that RF can be used as an effective method to predict the PPV. Moreover, RF is a more powerful method compared to other decision tree methods to determine the importance of input variables, as the RF considers a huge number of trees (e.g., 100) and prevents the masking of the tree by another correlated input. However, caution must be taken when applying the results to other studies, since the present study used only five input variables, including MC, HD, ST, PF, and D. Future studies can develop the RF model to predict the PPV using more or different input variables. Formal analysis, H.Z. ; conceptualization, D.J.A. ; supervision, J.Z. ; writing--review and editing, M.M.T., B.T.P., and D.J.A. ; validation, V.V.H. All authors have read and agreed to the published version of the manuscript.