###### Abstract  
Blasting is one of the primary aspects of the mining operations, and its environmental effects interfere with the safety of lives and property. Therefore, it is essential to accurately estimate the environmental impact of blasting, i.e., peak particle velocity (PPV). In this study, a regular random forest (RF) model was developed using 102 blasting samples that were collected from an open granite mine. The model inputs included six parameters, while the output is PPV. Then, to improve the performance of the regular RF model, five techniques, i.e., refined weights based on the accuracy of decision trees and the optimization of three metaheuristic algorithms, were proposed to enhance the predictive capability of the regular RF model. The results showed that all refined weighted RF models have better performance than the regular RF model. In particular, the refined weighted RF model using the whale optimization algorithm (WOA) showed the best performance. Moreover, the sensitivity analysis results revealed that the powder factor (PF) has the most significant impact on the prediction of the PPV in this project case, which means that the magnitude of the PPV can be managed by controlling the size of the PF. blasting; ground vibration; PPV prediction; random forest; whale optimization algorithm +
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that these methods are capable of handling high-dimensional problems, which means that the effects of multiple factors can be considered simultaneously when predicting PPV [15; 16]. However, many studies prove that the accuracy of statistical models is inferior compared to the ML methods [17; 18; 19; 20]. In recent years, a large number of ML techniques have been applied in different areas of geotechnics, such as tunnel construction and risk assessment [21; 22; 23; 24; 25; 26; 27; 28], soil classification [29], pile technology [30; 31], materials properties [32; 33; 34; 35], slope stability [36; 37], blasting environmental issues [38; 39], pillar stability prediction [40], and rock material properties [41; 42; 43; 44], which reveal the favorable application prospects of the ML techniques. Similarly, as for the research on PPV prediction, to compensate for the shortcomings of empirical formulas and statistical models, there has been a strong inclination toward using the ML techniques in predicting the PPV because of their feasible ability to handle multidimensional nonlinear problems. For instance, [PERSON] et al. [1] used five machine learning techniques (i.e., classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) to predict the PPV caused by mine blasting. Their research utilized five parameters, including maximum charge per delay (MC), stemming (ST), distance from the measuring station to the blast face (DI), powder factor (PF), and hole depth (HD), to develop the ML models. The results showed that the RF had a superior capability in predicting PPV compared with the other four techniques. [PERSON] [45] developed an ANN-based formula to predict the PPV using two inputs, i.e., the distance from the monitoring point to the blast face and the explosive charge per delay. This study attempts to construct an interpretable formula through the ANN to identify the effect of inputs on the PPV. [PERSON] et al. [46] compared the performance of two AI techniques (e.g., ANN and decision tree (DT)) for forecasting the blast-induced PPV. In the study, eight input parameters, i.e., total charge, number of holes, hole diameter, distance from blasting face, hole depth, tunnel cross section, the maximum charge per delay, and charge per hole, were used to design the ML models. The results indicated that the precision of the DT model performed better than the ANN model.