Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study

Acta Geophysica - Tập 67 - Trang 477-490 - 2019
Hoang Nguyen1,2, Xuan-Nam Bui1,2, Hoang-Bac Bui3,4, Dao Trong Cuong5
1Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Hanoi, Vietnam
2Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, Hanoi, Vietnam
3Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, Hanoi, Vietnam
4Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, Hanoi, Vietnam
5Ministry of Industry and Trade, Hanoi, Vietnam

Tóm tắt

Ground vibration is one of the most undesirable effects induced by blasting operations in open-pit mines, and it can cause damage to surrounding structures. Therefore, predicting ground vibration is important to reduce the environmental effects of mine blasting. In this study, an eXtreme gradient boosting (XGBoost) model was developed to predict peak particle velocity (PPV) induced by blasting in Deo Nai open-pit coal mine in Vietnam. Three models, namely, support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN), were also applied for comparison with XGBoost. To employ these models, 146 datasets from 146 blasting events in Deo Nai mine were used. Performance of the predictive models was evaluated using root-mean-squared error (RMSE) and coefficient of determination (R2). The results indicated that the developed XGBoost model with RMSE = 1.554, R2 = 0.955 on training datasets, and RMSE = 1.742, R2 = 0.952 on testing datasets exhibited higher performance than the SVM, RF, and KNN models. Thus, XGBoost is a robust algorithm for building a PPV predictive model. The proposed algorithm can be applied to other open-pit coal mines with conditions similar to those in Deo Nai.

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