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

Author

Listed:
  • Sunwen Du

    (College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    Shanxi Engineering Research Center for Green Mining, Taiyuan 030024, China)

  • Guorui Feng

    (College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    Shanxi Engineering Research Center for Green Mining, 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)

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.

Suggested Citation

  • Sunwen Du & Guorui Feng & Jianmin Wang & Shizhe Feng & Reza Malekian & Zhixiong Li, 2019. "A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data," Energies, MDPI, vol. 12(7), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1288-:d:219825
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    References listed on IDEAS

    as
    1. Zhenlu Shao & Deming Wang & Yanming Wang & Xiaoxing Zhong & Xiaofei Tang & Xiangming Hu, 2015. "Controlling coal fires using the three-phase foam and water mist techniques in the Anjialing Open Pit Mine, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 1833-1852, January.
    2. Li, Zhixiong & Wu, Dazhong & Hu, Chao & Terpenny, Janis, 2019. "An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 110-122.
    3. Ashkan Vaziri & Larry Moore & Hosam Ali, 2010. "Monitoring systems for warning impending failures in slopes and open pit mines," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 55(2), pages 501-512, November.
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