## 4 Conclusions and Future Works  
Prediction of AOp values is vital because of their negative impacts on people and construction near the blasting zone. In this paper, a hybrid learning model, the SVR-GO-BFS\({}_{n}\), was developed to forecast the AOp values. A summary of the significant findings of this study is provided below:  
* In incorporation with SVR, the BFS algorithm produced excellent forecasts on the dataset of this research concern.
* The GO algorithm was confirmed to efficiently function in exploring the optimal conditions of SVR's hyperparameters and achieving an accurate AOp prediction.
* The reason for this efficacy was that this algorithm possesses a compelling ability for finding and controlling the optimal answers to multi-scale issues.
* The developed model outperformed other established models in this study, including single SVR, CART, RF, ANN, and XGBT. Hence, the SVR-GO-BFS\({}_{n}\) can be a useful procedure for forecasting AOp values. Among the various optimizers used in this study, the GO optimizer outperformed the alternative ones, i.e., PSO, NNA, and COA.  
As shown by the findings of the present study, the performance of the model developed was better than others. This approach is usable by other studies in different domains. Nevertheless, the regression issue is to be resolved by the developed model and is confined to the AOp forecast. Moreover, it is vital to note that the outcomes of this model are limited to quarries with similar characteristics. Although GO is a powerful optimizer, this study only used it to tune the SVR hyperparameters. We used only one dataset, which may be a limitation of this study. Future studies should use more datasets to test our proposed model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Performance** & **SVR-GO-BFS\({}_{7}\)** & **SVR-PSO-BFS\({}_{7}\)** & **SVR-COA-BFS\({}_{7}\)** & **SVR-NNA-BFS\({}_{7}\)** \\ \hline R\({}^{2}\) & 0.9826 & 0.9796 & 0.9729 & 0.9747 \\ RMSE & 1.3315 & 1.8188 & 1.9547 & 1.6929 \\ MAE & 1.2108 & 1.6675 & 1.7475 & 1.5625 \\ MAPE & 1.1633 & 1.5997 & 1.7170 & 1.4740 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of various optimizers for AOp prediction.  
Figure 11: The outcomes of the developed model with various optimization methods.  
Following this discussion, the authors suggest that further investigations could be carried out. Further studies should implement this developed method to deal with other issues in the blasting and mining analysis. Supplementary analysis of AOp predictions in various areas is required to assure the generalization of the outcomes of the developed system. The employment of GO as an input selection technique should be examined, and, subsequently, its efficiency should be analyzed against the BFS.  
L.C. Methodology, Software, Formal analysis, Writing--original draft. P.G.A. Methodology, Writing--review and editing, Supervision. M.Z.T. Writing--review and editing, Supervision. D.J.A., Conceptualization, Software, Formal analysis, Writing--review and editing, Supervision. D.V.U. Writing--review and editing. M.Y., Conceptualization, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
Supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202103415).  
Not applicable.  
The data is available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.