## 3 Results and Discussion
### Performance Comparison  
The authors compared the performance of the developed SVR-GO-BFS? model with a single SVR model. All nine variables were used to train the single SVR model. The outcomes of this comparison are presented in Table 4. The SVR-GO-BFS? model achieved a notably lower MAPE value compared with the single SVR model. The value of MAPE improved by about 62% when the newly developed model was applied to the data. Furthermore, R\({}^{2}\) was enhanced by approximately 19%. RMSE and MAE were improved by 68.09% and 62.26%, respectively. Hence, for predictive precision, it can be assumed that SVR-GO-BFS? particularly beats the single SVR model for AOp forecasting in the selected granite quarry sites in Malaysia. The principal responsible for enhancing the prediction performance of the SVR-GO-BFS? model was SVR's parameter optimization by GO and employing BFS for input choice.  
This study also compared the achievement of the developed model with some well-known ML models, including Random Forest (RF), Artificial Neural Networks (ANN), Extreme Gradient Boosting Tree (XGBT), and Classification and Regression Trees (CART). Nevertheless, BFS and GO were not hybridized with these models. All models were trained using the full set of inputs (nine inputs). For XGBT, Eta and Lambada were set as 0.3, 1.0, and its objective function was reg:linear. For CART, the maximum tree depth was 7. Concerning ANN, a backpropagation procedure by the Levenberg-Marquardt training algorithm was employed for its optimization. Additionally, the ANN structure included a single hidden layer and 11 hidden nodes. Furthermore, the authors used a sigmoid activation function while the value of the learning rate was 0.2. Table 5 shows how these models compare to SVR-GO-BFS? in terms of how well they work.  
The RMSE, MAPE, and MAE values of the developed SVR-GO-BFS? model were less than all benchmark models. Among benchmark models, ANN showed a better performance in terms of both accuracy and errors. Instead, the worst model was CART, which achieved the lowest accuracy and highest errors. While the XGBT obtained better accuracy than the RF, the RF outperformed the XGBT in terms of errors. The results of this comparison confirmed that the developed SVR-GO-BFS? was statistically better than the models developed for comparison. For a better explanation, the predictive effectiveness of the developed BA-GO-BFS? is demonstrated in Figure 9. The figure showed that the predicted data effectively track the real data with insignificant differences. The results of the performance criteria in Table 4 showed that the values of the error metrics were comparably low. The results of AOp predictions by SVR-GO-BFS? and other ML models are presented in Figure 10. The advantage of the developed SVR-GO-BFS? model was justified through the outcomes of the comparative evaluation. So, the importance of combining methods (SVR, GO, and BFS) is confirmed in the right way.  
\begin{table}
\begin{tabular}{c c c} \hline
**Performance Criterion** & **SVR-GO-BFS?** & **Single SVR** \\ \hline R\({}^{2}\) & 0.9826 & 0.8245 \\ RMSE & 1.3315 & 4.1728 \\ MAE & 1.2108 & 3.2083 \\ MAPE & 1.1633 & 3.0527 \\ \hline \end{tabular}
\end{table}
Table 4: Performance comparison between single SVR and SVR-GO-BFS?.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Performance Criterion** & **SVR-GO-BFS?** & **ANN** & **CART** & **RF** & **XGBT** \\ \hline R\({}^{2}\) & 0.9826 & 0.9767 & 0.5192 & 0.8874 & 0.9342 \\ RMSE & 1.3315 & 1.7206 & 7.0700 & 3.3300 & 2.5632 \\ MAE & 1.2108 & 1.4767 & 5.3168 & 2.9808 & 2.3600 \\ MAPE & 1.1633 & 1.3927 & 5.2427 & 2.8692 & 2.3240 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison between the SVR-GO-BFS? and other models.