## 5 Conclusions  
In this paper, three AI models of GPR, ELM and BPNN were developed and applied to predict blast-induced PPV. In showing the predictive capabilities of these AI techniques, a MARS and MVRA model were also developed. In developing and evaluating these models, 101 datasets obtained from Ras Limestone Mine of Shree Cement, India were utilized. Out of the 101 datasets, 81 were utilized to create the various models, while the remaining 20 were used as test sets for the models that were developed. The input parameters in the creation of the various models were average depth (m), burden (m), spacing (m), powder factor (t/kg), the distance between the monitoring station and the blasting site (m), stemming length (m), and maximum charge per delay (kg), while the output parameter was PPV. The various developed models were then evaluated using performance metrics of \(R\), \(R^{2}\), MSE and VAF. The results obtained showed that the GPR model had the lowest MSE of 0.0903, and the highest \(R\), \(R^{2}\), and VAF values of 0.9985, 0.9971 and 99.1728% respectively,  
Figure 8: The Input Parameters and PPV Relationship’s Strength.  
indicating that it was superior to the other models in predicting blasting-induced ground vibration. This was followed by MARS which had MSE, \(R\), \(R^{2}\) and VAF values of 0.1038, 0.9953, 0.9906 and 98.8692% respectively. Then ELM had an MSE of 0.1381, \(R\)-value of 0.9957, \(R^{2}\) value of 0.9915 and VAF value of 98.5469. Then the BPNN with an MSE, \(R\), \(R^{2}\) and VAF of 0.1714, 0.9924, 0.9848 and 98.2273% respectively. The MVRA performed very poorly as it had, with the highest MSE of 3.2456, and lowest \(R\)-value of 0.8310, the \(R^{2}\) value of 0.6906 and the VAF value of 66.0603%. The results obtained show that the GPR model can be utilized to forecast blast-induced ground vibration in the mining industry. The sensitivity analysis of the dataset found that spacing, a maximum charge per delay, distance and stemming length had a great influence on PPV whereas burden, powder factor and average depth had slight to no influence on PPV.  
Conceptualization, E.T.M., R.M.B., C.K.A., M.K.; methodology, R.M.B., C.K.A.; software, R.M.B., C.K.A.; formal analysis, R.M.B., C.K.A.; resources, E.T.M., R.M.B., C.K.A.; data curation, R.M.B. writing--original draft, M.B., E.T.M., R.M.B., C.K.A., M.K., M.M.S.S., S.K.; writing--review and editing, M.B., E.T.M., R.M.B., C.K.A., M.K., M.M.S.S., S.K.; Supervision, E.T.M., M.K., S.K.; funding acquisition, M.M.S.S. All authors have read and agreed to the published version of the manuscript.  
The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program 'Priority 2030' (Agreement 075-15-2021-1333 dated 30 September 2021).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Authors are thankful to [PERSON], Assistant Vice President and Management of Shree Cement, Beauwar, Rajashthan for providing data for the preparation of this paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**Abbreviations** & **Explanations** \\ ABC & Artificial bee colony \\ ANN & Artificial neural network \\ BA & Bat-inspired Algorithm \\ BN & Bayesian network \\ BBO & Biogeography-based optimization \\ BI & Blastability index (compressive strength/tensile strength) \\ BIENN & Brain-inspired emotional neural network \\ B & Burden \\ BS & Burden spacing ratio \\ CHAID & Chi-square automatic interaction detector \\ CART & Classification and regression tree \\ H & Distance between blasting face and monitoring point (m) \\ XGBoost & Extreme gradient boosting machine \\ ELM & Extreme learning machine \\ FFA & Firefly algorithm \\ FIS & Fuzzy inference system \\ FL & Fuzzy logic \\ GPR & Gaussian process regression \\ GEP & Gene expression programming \\ GA & Genetic algorithm \\ GP & Genetic programming \\ \end{tabular}