Evaluation of flyrock phenomenon due to blasting operation by support vector machine
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Flyrock is an undesirable phenomenon in the blasting operation of open pit mines. Flyrock danger zone should be taken into consideration because it is the major cause of considerable damage on the nearby structures. Even with the best care and competent personnel, flyrock may not be totally avoided. There are several empirical methods for prediction of flyrock phenomenon. Low performance of these models is due to complexity of flyrock analysis. Support vector machine (SVM) is a novel machine learning technique usually considered as a robust artificial intelligence method in classification and regression tasks. The aim of this paper is to test the capability of SVM for the prediction of flyrock in the Soungun copper mine, Iran. Comparing the obtained results of SVM with that of artificial neural network (ANN), it was concluded that SVM approach is faster and more precise than ANN method in predicting the flyrock of Soungun copper mine. © 2011 Springer-Verlag London Limited.