## 1 Introduction  
Ground vibration is one of the main adverse blasting outcomes that has received significant attention in the mining and civil industries [1; 2]. Ground vibration is known to have a lot of adverse impacts on the environment (cracks on building structures) and the stability of pit walls. It is worth mentioning that several factors contribute to the occurrence of these blast-induced ground vibrations. These factors can be categorized into controllable factors and uncontrollable factors [3; 4]. The controllable factors are those that the blast engineer has control over and can change. These include the blast design parameters of stemming length, hole depth, spacing, burden, hole inclination and explosive parameters of delay timings, a maximum charge per delay, and total charge. The uncontrollable factors are those the blast engineer has no control over, and they include both geotechnical and geomechanical parameters such as rock strength, faults, and folds [5; 6; 7; 8; 9]. The peak particle velocity (PPV) is the index for assessing ground vibration induced by blasting [10]. When detonation of explosives takes place, high energy is released in the blast hole which fractures the rock surrounding the blasthole [11]. Some of the energy released is used to fragment and displace the rock mass. The rest of the energymove through the ground as ground vibration andimpacts surrounding structures. Due to the adverse impact of blast-induced ground vibration, it has always been in the interest of the blast engineer to model and predicts its occurrence to minimize vibration level as much as possible. In that regard, a lot of research has been conducted since the 1950s [12] to develop models for predicting ground vibration arising out of blasting operations. These models have been developed using empirical techniques through to the use of artificial intelligence (AI) techniques [13]. These AI techniques have been found to produce more accurate results than the empirical techniques and hence have received worldwide attention due to their unique capabilities [14]. AI techniques that have been developed and used in the prediction of blasting outcomes (ground vibration, air overpressure, and flyrock) are outlined in Table 1. It is worth noting that all abbreviations used in this work are presented in the Abbreviations Section. More recently in ground vibration studies, other researchers have applied evolutionary and metaheuristic optimization algorithms to optimize simple AI techniques. Some of these works are presented in Table 2.  
Table 3 provides a detailed summary of some research on ground vibration prediction. \begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Methods** & **Application** \\ \hline
[3; 15; 20; 21; 22; 23; 24; 25; 26; 27; 28] & FL, SVR, ANFIS, ANN, CART, GPR, ICA, & Ground Vibration Prediction \\  & SVM, ELM, GEP, PSO, BN & \\ \hline
[29; 30; 31; 32; 33] & PSO-ANN, FIS, ANN, ICA\_ANN, & \\  & BIENN, GP, M5 DT, SVM, KNN, CHAID & Air Overpressure Prediction \\ \hline
[34; 35; 36; 37; 38; 39] & PSO-ANN, RF, BN, BBO-ELM, OREL, & \\  & ELM, WOA-SVM, GP & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: AI Models developed and applied to predict ground vibration, air overpressure and flyrock. \begin{table}
\begin{tabular}{c c} \hline \hline
**References** & **Hybrid Models** \\ \hline PSO-ANN, ICA-ANN, ABC-ANN, PSO-ANFIS, ICA-FIS, & \\ FFA-ANN, GA-ANFIS, PSO-ANFIS, PSO-XGBoost, GA-SVR, & \\ PSO-SVR, FFA-SVR, GA-ANN, GWO-RVR, BAT-RVR, HHOA-RF, & \\ ICA-XGBoost, ICA-MSDT, HHOA-ELM, GOA-ELM & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hybrid Models developed and applied to predict ground vibrations. Nevertheless, the application of single AI techniques is still of interest in this ever-growing technological world. ANN has been developed by [69] to predict the earth surface deformation.