A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data

Sunwen Du, Guorui Feng, Jianmin Wang, Shizhe Feng, Reza Malekian and Zhixiong Li
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Sunwen Du: College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Guorui Feng: College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Jianmin Wang: College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Shizhe Feng: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
Reza Malekian: Department of Computer Science and Media Technology, Malmö University, 20506 Malmö, Sweden
Zhixiong Li: School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia

Energies, 2019, vol. 12, issue 7, 1-15

Abstract: Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners.

Keywords: ensemble learning; slope deformation; prediction model; safety (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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