A stacked multiple kernel support vector machine for blast induced flyrock prediction

As a widely used rock excavation method in civil and mining construction works, the blasting operations and the induced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one of the most important issues induced by blasting operations, since the accurate prediction of which is crucial for delineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets of blasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stacked MK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve the model performance by addressing the importance level of different features. For comparison purpose, 6 other machine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), Artificial Neural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validation process for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVM model achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95 and 99.25 in training and testing phase, respectively.