## 7 Conclusions and Recommendations  
Drilling-blasting is an essential task in mining operations. Until now, it is still the most efficient method for fragmenting rocks in open-cast mines. However, the impact on the environment caused by drilling operations as well as blasting operations is inevitable, especially in dust concentration. Evidence of the dangers of PM\({}_{10}\) has been found in open-pit mines. Therefore, the prediction and strict control of PM\({}_{10}\) concentrations is necessary to minimize the harmful impact on the surrounding \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Performance**} & \multicolumn{6}{c}{**Importance Parameters**} \\ \cline{2-10}  & **RMSE** & **R\({}^{2}\)** & \(d\)** & \(P\)** & \(W_{m}\)** & \(S\)** & \(\rho\)** & \(\sigma_{e}\)** & \(R\) \\ \hline RF & 0.060 & 0.894 & 0.018 & 0.825 & 0.927 & 0.008 & 0.017 & 0.779 & 0.013 \\ CART & 0.052 & 0.924 & 0.019 & 0.824 & 0.929 & 0.007 & 0.017 & 0.784 & 0.012 \\ KNN & 0.067 & 0.867 & 0.024 & 0.802 & 0.896 & 0.006 & 0.022 & 0.826 & 0.014 \\ PSO-SVR-L & 0.040 & 0.954 & 0.018 & 0.825 & 0.975 & 0.006 & 0.018 & 0.817 & 0.012 \\ PSO-SVR-P & 0.042 & 0.948 & 0.017 & 0.836 & 0.957 & 0.007 & 0.018 & 0.812 & 0.013 \\ PSO-SVR-RBF & 0.043 & 0.946 & 0.016 & 0.839 & 0.955 & 0.006 & 0.018 & 0.813 & 0.013 \\ \hline \hline \end{tabular} Note: drilling diameter/diameter of borehole (\(d\)), penetration rate of the drill (\(P\)), moisture content (\(W_{m}\)), silt content (\(S\)), density of rock mass (\(\rho\)), compressive strength (\(\sigma_{e}\)), rebound hardness number (\(R\)), particulate matter 10 micrometers (PM\({}_{10}\)), root-mean-squared error (RMSE), determination coefficient (R\({}^{2}\)).  
\end{table} 
Table 6: Important parameters of the models in this study. Figure 15: Measured versus predicted values of PM\({}_{10}\) concentration on the testing dataset. environment. In this study, PM\({}_{10}\) concentration from drilling operations in open-pit mines was considered and evaluated. In this study, some conclusions and remarks were drawn:  
1. The dust concentration caused by activities in open-pit mines is high and very dangerous. The dust is the cause of the harmful impact on the environment and public health. In this study, PM\({}_{10}\) concentration from drilling operations in open-pit mines was considered and predicted. However, the dust concentration caused by other activities in open-pit mines also needs to be controlled and predicted in the future. Total dust concentration needs to be managed in open-pit mines, and is a challenge for future works. 2. For drilling operations, AI techniques are the advanced methods for predicting drill-induced dust concentration. They provided predictive intelligence models with high accuracy in practical engineering. In addition to the ANN, which was developed by previous researchers, the other machine learning techniques, i.e., RF, CART, KNN, PSO-SVR in this study were also techniques to control air quality in open-pit mines. 3. The PSO algorithm is a robust tool for the optimization of the SVR model for estimating PM\({}_{10}\) concentration. With an RMSE of 0.040 and R\({}^{2}\) of 0.954, the proposed PSO-SVR-L model was the most dominant model for predicting drill-induced PM\({}_{10}\) concentration in this study. It should be applied in practical engineering to control PM\({}_{10}\) concentration from drilling operations as well as the other processes. Additionally, the CART, RF, and KNN models should also be considered in other conditions of different sites for predicting dust concentration. They can be good models in other case studies for predicting the environmental issues in open-pit mines. 4. \(\rho\), \(W_{m}\), and P are the most influential parameters on the PM\({}_{10}\) concentration predictive model, especially \(W_{m}\). They should be of particular interest and carefully collected for predicting drill-induced PM\({}_{10}\) concentration. 5.