## 1 Introduction
Thus, the predictive capacities of three artificial intelligence algorithms, backpropagation neural network (BPNN), ELM, and GPR, are investigated in this study using blasting data from a quarry (Ras Limestone Mine of Shree Cement) in India to estimate PPV values. A multivariate adaptive regression spline (MARS) approach, as well as a multivariate regression analysis (MVRA) model, was developed and used for comparison purposes. Studies have been made to compare the GPR and BPNN [22], MARS and BPNN [67], ELM and BPNN [70], GP and MARS [63], GPR and MVRA [71] and BPNN and MVRA [70]. However, little has been done in the literature to compare the predictive performance of GPR, MARS, BPNN, ELM and MVRA in ground vibration prediction studies. In that regard, this study is exploratory. It is worth mentioning that the empirical models developed for predicting blast-induced ground vibration were not considered \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Technique**} & \multicolumn{6}{c}{**Input Parameters**} & \multicolumn{2}{c}{**No. of**} \\ \cline{3-8}  & & **Rock Mass** & **Blast Design** & **Explosives** & **Other** & **Datasets** & **R2** \\ \hline [55] & ANN & \(\
u\), BI, E, Pv & HD, B, S & VOD, Q & H & 154 & 0.9864 \\ \hline [15] & FIS & - & - & Q & H & 33 & 0.92 \\ \hline [56] & ANN & & HD & Q & H & 162 & 0.9493 \\ \hline [57] & SVM, ANN & - & - & Q & H & 37 & \begin{tabular}{c} SVM = 0.89, \\ ANN = 0.85 \\ \end{tabular} \\ \hline [16] & FIS & - & B, S, ST & Q & H & 120 & 0.95 \\ \hline [58] & ANN & - & - & Q & H & 20 & 0.93 \\ \hline [40] & ANN-PSO & RD & \begin{tabular}{c} B, S, N, HD, \\ SD \\ \end{tabular} & Q & H & 44 & 0.94 \\ \hline [59] & ANN & & ST, HD & Q & H & 69 & 0.957 \\ \hline [60] & ANN & - & HD, ST & Q & H & 115 & 0.98 \\ \hline [28] & ANN-PSO & RQD & ST, BS, SD & PF, Q & H & 88 & 0.89 \\ \hline [61] & GA-ANN, & - & - & Q & H, RD & 70 & \begin{tabular}{c} GA-ANN = 0.988, \\ ANFIS = 0.92 \\ \end{tabular} \\ \hline [62] & \begin{tabular}{c} WNN, \\ GMDH, \\ ANN \\ \end{tabular} & - & HD, NH & PF, Q & H & 210 & \begin{tabular}{c} WNN = 0.712, \\ GMDH = 0.684, \\ ANN = 0.729 \\ \end{tabular} \\ \hline [63] & \begin{tabular}{c} GP, RSM, \\ MARS \\ \end{tabular} & & & Q & H & 200 & \begin{tabular}{c} GP = 0.7864, \\ RSM = 0.7832, \\ MARS = 0.8056 \\ \end{tabular} \\ \hline [64] & ANFIS & - & - & Q & H & 90 & 0.983 \\ \hline [65] & ANN & - & - & Q & H & 68 & 0.955 \\ \hline [66] & ANN & & & PF, Q & H & 88 & 1 \\ \hline [22] & GPR, ANN & - & HD, NH & PF, Q & H & 210 & \begin{tabular}{c} GPR = 0.695, \\ ANN = 0.688 \\ \end{tabular} \\ \hline [49] & \begin{tabular}{c} SaDE-ELM, \\ ELM, ANN \\ \end{tabular} & - & HD, NH & PF, Q & H & 210 & \begin{tabular}{c} SaDE-ELM = 0.759, \\ ELM = 0.728, \\ ANN = 0.729 \\ \end{tabular} \\ \hline [67] & MARS, ANN & - & HD, NH & PF, Q & H & 210 & \begin{tabular}{c} MARS = 0.7074, \\ ANN = 0.6879 \\ \end{tabular} \\ \hline [68] & \
\begin{tabular}{c} LSSVM, \\ ANN \\ \end{tabular} & - & HD, NH & PF, Q & H & 210 &
\begin{tabular}{c} LSSVM = 0.73, \\ ANN = 0.729 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Input parameters, size of data and AI techniques for prediction of ground vibration. in this study. The reason is that studies done by [17; 40; 53; 57; 59; 72; 73] have proved that these empirical models do not produce accurate results. The models used in this study consider seven effective parameters, namely the average depth, a maximum charge per delay, powder factor, spacing, burden, distance and stemming length, because, as shown in [5; 6; 7], they significantly affect the intensity of ground vibration.