A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET

Fly-rock caused by blasting is one of the dangerous side effects that need to be accurately predicted in open-pit mines. This study proposed a new technique to predict the distance of fly-rock based on an ensemble of support vector regression models (SVRs) and Lasso and elastic-net regularized generalized linear model (GLMNET), called SVRs-GLMNET. It was developed based on a combination of six SVR models and a GLMNET model. Accordingly, the dataset including 210 experimental data was divided into three parts, i.e., training, validating, and testing. Of the whole dataset, 70% was used for the development of the six SVR models first as the sub-models. Subsequently, 20% of the entire dataset (the validating dataset) was used to predict fly-rock based on the six developed SVR models. The predicted results from the six developed SVR models were used as the input variables to establish the GLMNET model (i.e., SVRs-GLMNET model). Finally, the remaining 10% of the dataset was used for testing the performance of the proposed SVRs-GLMNET model. A comparison and evaluation of the six developed SVR models and the proposed SVRs-GLMNET model were implemented based on five statistical criteria, such as mean absolute error (MAE), mean absolute percentage error (MAPE), root-mean-square error (RMSE), variance account for (VAF), and determination of correlation (R-2). The results indicated that the proposed SVRs-GLMNET model provided the most dominant performance in predicting the distance of fly-rock caused by bench blasting in this study with an RMSE of 3.737, R-2 of 0.993, MAE of 3.214, MAPE of 0.018, and VAF of 99.207. Whereas, the other models yielded poorer accuracy with RMSE of 7.058-12.779, R-2 of 0.920-0.972, MAE of 3.438-7.848, MAPE of 0.021-0.055, and VAF of 90.538-97.003.

Automated summary

A new technique, SVRs-GLMNET, was proposed to accurately predict the distance of fly-rock in open-pit mines by combining SVR models and a GLMNET model. The study divided the dataset into training, validating, and testing sets, ultimately showing that SVRs-GLMNET outperformed other models in predicting fly-rock distance.