## 6 Results and Discussion  
Once the PM\({}_{10}\) concentration predictive models were developed, their performance was evaluated and discussed through RMSE and R\({}^{2}\), as mentioned in Equations (11) and (12). Table 5 shows the performance of the developed models on the training and testing datasets. Table 5 showed that the PSO-SVR models performed very well in estimating PM\({}_{10}\) concentration from drilling operations in this study with an RMSE in the range of 0.036 to 0.041 on the training dataset, 0.040 to 0.043 on the testing dataset; R\({}^{2}\) in the range of 0.962 to 0.963 on the training dataset, 0.946 to 0.954 on the testing dataset. Notable, the PSO-SVR-L model yielded the best performance with an RMSE of 0.036 and 0.040 for the training and testing dataset, respectively; and R\({}^{2}\) of 0.963 and 0.954 for the training and testing dataset, respectively. With the highest performance, the PSO-SVR-L model is the most dominant model for estimating PM\({}_{10}\) concentration in this study. The remaining AI models (RF, CART, KNN) provided more mediocre performance than those of the PSO-SVR models with an RMSE in the range of 0.057 to 0.074 on the training dataset, 0.052 to 0.067 on the testing dataset; and R\({}^{2}\) in the range of 0.904 to 0.963 on the training dataset, 0.867 to 0.924 on the testing dataset. Remarkable, the KNN model yielded the most inferior performance with an RMSE of 0.074 and 0.067 for the training and testing dataset, respectively; and R\({}^{2}\) of 0.904 and 0.867 for the training and testing dataset, respectively. Figure 15 shows the measured versus predicted values of PM\({}_{10}\) concentration on the testing dataset. Based on the obtained results from the developed models, it can be seen that machine learning algorithms perform very well in estimating PM\({}_{10}\) concentration from drilling operations in open-pit mines. In particular, the PSO algorithm played a significant role in optimizing the hyper-parameters of the SVR models to provide more accurate predictive results. The PSO-SVR-L model was introduced as the most superior model in this study for estimating PM\({}_{10}\) concentration that explained the linear relationship of the input variables with PM\({}_{10}\) concentration. However, the present study used seven independent/input variables for predicting PM\({}_{10}\), and the effectiveness of the models was different. Therefore, an importance analysis procedure of the independent/input variables for each model was performed in this study, i.e., using the Hilbert-Schmidt Independence Criterion (HSIC) [99; 100]. An overall picture of performance as well as the ability to explain the relationship between the independent/input variables of the models is illustrated in Table 6.  
From Table 6, it is easy to recognize that \(P\), \(W_{tn}\), and \(\sigma_{c}\) are the most importance parameters of all developed AI models. They should be used for estimating PM\({}_{10}\) concentration from drilling operations. Taking a closer look at Table 6 shows that the assessment of the importance of input variables has a significant effect on the performance of the model. In the proposed PSO-SVR-L model, the \(W_{tn}\) parameter was most importance parameter with indices of 0.975, whereas the remaining models were only evaluated in the range of 0.896 to 0.957. Likewise, \(P\) and \(\sigma_{c}\) also received different values from different models. It can be seen that the evaluation of the importance of input variables as too low or too high has a significant impact on the performance of the forecasting model.