A SVR-GWO technique to minimize flyrock distance resulting from blasting

Flyrock is one of the most important environmental and hazardous issues in mine blasting, which can affect equipment and people, and may lead to fatal accidents. Therefore, prediction and minimization of this phenomenon are crucial objectives of many rock removal projects. This study is aimed to predict the flyrock distance with the use of machine learning techniques. The most effective parameters of flyrock were measured during blasting operations in six mines. In total, 262 data samples of blasting operations were accurately measured and used for approximation purposes. Then, flyrock was evaluated and estimated using three machine learning methods: principle component regression (PCR), support vector regression (SVR), and multivariate adaptive regression splines (MARS). Many models of PCR, SVR, and MARS were constructed for the flyrock distance prediction. The modeling process of each method is elaborated separately in a way to be used by other researchers. The most important parameters affecting these models were assessed to obtain the best performance for the developed models. Eventually, a preferable model of each machine learning technique was used for comparison purposes. According to the used performance indices, coefficient of determination (R2), and root mean square error, the SVR model showed a better performance capacity in predicting flyrock distance compared with the other proposed models. Thus, the SVR prediction model can be used to accurately predict flyrock distance, thereby properly determining the blast safety area. Additionally, the SVR model was optimized by new optimization algorithm namely gray wolf optimization (GWO) for minimizing the flyrock resulting from blasting operation. By developing optimization technique of GWO, the value of flyrock can be decreased 4% compared with the minimum flyrock distance.