Flyrock, rock fragments thrown to an excessive distance, is an undesirable phenomenon and an ongoing problem in open pit blasting operation. Flyrock danger zone should taking into consideration while 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 projectile distance which low performance of these models is due to complexity of flyrock analysis. Support vector machine (SVM) is a novel machine learning technology to be 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 projectile distance in the Soungun copper mine in Iran. Comparing the obtained results of SVM with those given by artificial neural network (ANN) have indicated that the SVM approach is faster and is more precise than the ANN method in prediction of flyrock projectile distance of Soungun copper mine.
