Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities, causing harmful effects on the surrounding environment as well as the nearby residents. Both the local ecology and human communities suffer from the consequences of these vibrations. Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity ( PPV ) and frequency, which are essential parameters for measuring vibration velocity. Accurate prediction of vibration occurrence is critically important. Therefore, this study employs five machine learning models for predicting the PPV and frequency resulting from quarry blasting. This work compares five machine learning models (XGBoost, Catboost, Bagging, Gradient Boosting, and Random Forest Regression) to choose the most efficient performance model. The performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase, there was a strong correlation observed between the actual and the predicted ones. The analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R 2 = 0.983, MSE = 0.000078, RMSE = 0.008, NRMSE = 0.019, MAD = 0.004, MAPE = 35.197 in the PPV prediction, and R 2 = 0.975, MSE = 0.000243, RMSE = 0.015, NRMSE = 0.031, MAD = 0.008, MAPE = 37.281 for the frequency prediction. This study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration, and frequency. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency. By employing machine learning models, this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting. • Five machine learning models are implemented for the prediction of PPV and frequency in rock blasting. • Different data visualization and data processing techniques are takes place to understand the data in detail and detect outliers. • The Catboost regression model outperforms with better performance prediction for both PPV and frequency.