A Comparative Study of Machine Learning Methods for Prediction of Blast-Induced Ground Vibration

IF 1.1 Q3 MINING & MINERAL PROCESSING
A. Srivastava, B. Choudhary, M. Sharma
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引用次数: 2

Abstract

Blast-induced ground vibration (PPV) evaluation for a safe blasting is a long-established criterion used mainly by the empirical equations. However, the empirical equations are again considering a limited information. Therefore, using Machine Learning (ML) tools [Support Vector Machine (SVM) and Random Forest (RF)] can help in this context, and the same is applied in this work. A total of 73 blasts are monitored and recorded in this work. For the ML tools, the dataset is divided into the 80-20 ratio for the training and testing purposes in order to evaluate the performance capacity of the models. The prediction accuracies by the SVM and RF models in predicting the PPV values are satisfactory (up to 9% accuracy). The results obtained show that the coefficient of determination (R2) for RF and SVM is 0.81 and 0.75, respectively. Compared to the existing linear regressions, this work recommends using a machine learning regression model for the PPV prediction.
爆炸诱发地面振动预测的机器学习方法比较研究
安全爆破的爆致地面振动(PPV)评价是一个建立已久的准则,主要采用经验方程。然而,经验方程再次考虑了有限的信息。因此,使用机器学习(ML)工具[支持向量机(SVM)和随机森林(RF)]可以在此背景下提供帮助,并且同样适用于本工作。在这项工作中,共监测和记录了73次爆炸。对于ML工具,数据集被分成80-20的比例用于训练和测试目的,以评估模型的性能能力。SVM和RF模型对PPV值的预测精度均达到了9%。结果表明,射频和支持向量机的决定系数R2分别为0.81和0.75。与现有的线性回归相比,这项工作建议使用机器学习回归模型进行PPV预测。
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来源期刊
Journal of Mining and Environment
Journal of Mining and Environment MINING & MINERAL PROCESSING-
CiteScore
1.90
自引率
25.00%
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0
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