applied sciences Article  
Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1,*  
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proposed a new formula to calculate PPV that included a K-exponential. [PERSON] et al. [5] investigated the predictor equations for determining the parameters of PPV. However, due to the complex geological environments of mines and differences between types of explosives, the accuracy of the prediction results was limited. With the development of computer technology, machine learning (ML) methods gained significant popularity [6; 7; 8]. Researchers have undertaken PPV regression predictions using artificial intelligence (AI) models; the corresponding research status is illustrated in Table 1.  
As shown in Table 1, conventional ML algorithms, neural networks, and many AI-based soft computing models have been introduced to predict PPV. However, while the PPV of open-pit mines has been investigated in past decades [9; 10; 11; 12; 13; 14; 15; 16; 17], there is less ground vibration prediction research regarding underground mines, and the prediction accuracy is lower [18; 19]. Unlike open-pit mines, the surface structures of underground mines are constructed around the ore body. As a result, residential areas are in closer proximity to the mining zone and are therefore exposed to more intense blasting vibrations. In addition, in open-pit mines, only the straight-line distance between the monitoring point and the blasting point is considered for the shallow surface ore bodies, while underground mines are deeper and the overlying rock layer at the blasting point is thicker, affecting the propagation of blasting vibrations. Therefore, the effect of blasting depth on surface blasting vibrations must be considered independently, and it is necessary to investigate the prediction of PPV for underground mines. This study employed AI-based models to predict the PPV of underground mines based on three ML regression models (a random forest regressor (RFR), a bagging regressor (BR), and a gradient boosting regressor (GBR)) and three AI optimization algorithms (a genetic algorithm (GA), the gray wolf optimizer (GWO), and a particle swarm optimization (PSO)). In addition, nine AI-based models (GWO-RFR, GA-RFR, PSO-RFR, GWO-BR, GA-BR, PSO-BR, GWO-GBR, GA-GBR, and PSO-GBR) were used to predict the X-PPV, Y-PPV, and Z-PPV. A regression prediction of blast vibrations in underground mines was carried out.