ORELM: A Novel Machine Learning Approach for Prediction of Flyrock in Mine Blasting

被引:0
作者
Xiang Lu
Mahdi Hasanipanah
Kathirvel Brindhadevi
Hassan Bakhshandeh Amnieh
Seyedamirhesam Khalafi
机构
[1] China University of Mining and Technology,School of Mines
[2] China University of Mining and Technology,State Key Laboratory of Coal Resources and Safe Mining
[3] Duy Tan University,Institute of Research and Development
[4] University of Kashan,Department of Mining Engineering
[5] Ton Duc Thang University,Innovative Green Product Synthesis and Renewable Environment Development Research Group, Faculty of Environment and Labour Safety
[6] University of Tehran,School of Mining, College of Engineering
[7] University of Houston,Department of Construction Management
来源
Natural Resources Research | 2020年 / 29卷
关键词
Blasting; Flyrock; Extreme learning machine; Outlier robust extreme learning machine;
D O I
暂无
中图分类号
学科分类号
摘要
Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models.
引用
收藏
页码:641 / 654
页数:13
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