## 1 Introduction  
Drilling and blasting are important parts of an effective method to excavate the hard rock in the mining industry. One of the fundamental problems induced by blasting is the ground vibration (see Figure 1). Therefore, the ability to make accurate predictions of the ground vibration is a crucial need in this field. As shown in Figure 2, body waves including the compressional P-wave, transverse S-wave, and surface waves including the Love wave (Q-wave) and the Rayleigh wave (R-wave) are generatedby blasting. It is worth mentioning that the majority of the energy is transmitted by the R-wave, and the P-wave has the fastest speed. According to the literature [1; 2; 3; 4] the intensity of ground vibration can be measured based on some descriptors, including the frequency and peak particle velocity (PPV). Among them, PPV is accepted globally and is used in many studies to evaluate the blast-induced ground vibration [5; 6]. In the production cycle of mines, PPV plays a significant role during blasting operations and may result in undesirable effects in terms of anthropogenic hazards. Thus, a precise prediction of PPV is very crucial in terms of process and health safety. This key parameter is highly dynamic and non-linear because  
Figure 1: Waves generated by blasting. Figure 2: A view of the P, S, and R waves generated by blasting. it depends upon various process attributes. Therefore, the estimation of PPV through modeling is a challenging and haphazard task. A review of previous studies [7; 8] revealed that two categories of parameters significantly affect PPV: the blasting design and rock properties. As can be guessed, the blasting design parameters such as burden, weight charge per delay, stemming, and powder factor are controllable, while the rock properties parameters such as the tensile strength of the rock mass are uncontrollable and cannot be changed by engineers. In the past, various physical-based models have been used to study the blasting operations and simulations in order to minimize environmental damage. These types of physical models made use of governing formulations to account for blasting parameters. However, these governing equations have some limitations. The main limitation is that they consider ideal conditions rather than the conditions which exist in real-life situations. Additionally, the physical-based studies are typically costlier since they require a more experimental set-up as well as experts who understand complex mathematical formulations. In such circumstances, when the number of variables is limited and forecasting is more crucial than understanding the underlying causative mechanism, pattern recognition-based models are a viable tool. In order to overcome the problem stated above and establish the relationship between the blasting process through pattern recognition, the researchers used different ML techniques that can mimic and establish relationships to obtain a higher prediction accuracy. A literature review showed that a wide range of recently-published papers have demonstrated soft computing methods for predicting aims in different fields [9; 10; 11; 12; 13; 14; 15; 16; 17; 18], especially for predicting the PPV, such as an artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). [PERSON] [19] compared SVM and empirical models to predict the PPV, with a better result for the former. In another study, [PERSON] et al. [20] predicted the blast-induced PPV using SVM and a general regression neural network (GRNN) with the conclusion that SVM is a promising alternative tool for both empirical and GRNN models. On the other hand, [PERSON] et al. [21] explored the potential application of fuzzy system (FIS) and empirical models using 120 samples. The result showed that FIS predicts PPV more accurately than the empirical models. [PERSON] et al. [22] compared ANFIS, and ANN, as two well-known soft computing models, to predict PPV, then the result was compared to that of empirical models.