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

College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Shanxi Engineering Research Center for Green Mining, Taiyuan 030024, China
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
Department of Computer Science and Media Technology, Malmö University, 20506 Malmö, Sweden
School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Author to whom correspondence should be addressed.
Energies 2019, 12(7), 1288;
Received: 18 February 2019 / Revised: 21 March 2019 / Accepted: 28 March 2019 / Published: 3 April 2019
(This article belongs to the Special Issue Analysis for Electrical Machines Monitoring)
PDF [3737 KB, uploaded 3 April 2019]


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. View Full-Text
Keywords: ensemble learning; slope deformation; prediction model; safety ensemble learning; slope deformation; prediction model; safety

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Du, S.; Feng, G.; Wang, J.; Feng, S.; Malekian, R.; Li, Z. A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data. Energies 2019, 12, 1288.

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