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Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study

Author

Listed:
  • Jiang Li

    (School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Zhuoying Tan

    (School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Naigen Tan

    (School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Aboubakar Siddique

    (School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Jianshu Liu

    (School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Fenglin Wang

    (Hebei Iron & Steel Group, Luanxian Sijiaying Iron Mine Co., Ltd., Tangshan 063700, China)

  • Wantao Li

    (Hebei Iron & Steel Group, Luanxian Sijiaying Iron Mine Co., Ltd., Tangshan 063700, China)

Abstract

Slope stability and landslide analysis in open-pit mines present significant engineering challenges due to the complexity of predisposing factors. The Sijiaying Iron Mine has an annual production capacity of 21 million tons, with a mining depth reaching 330 m. Numerous small-scale landslides have occurred in the shallow areas. This study identifies four key factors contributing to landslides: topography, engineering geology, ecological environment, and mining engineering. These factors encompass both microscopic and macroscopic geological aspects and temporal surface displacement rates. Data are extracted using ArcGIS Pro 3.0.2 based on slope units, with categorical data encoded via LabelEncoder. Multivariate polynomial expansion is applied for data coupling, and SMOTENC–TomekLinks is used for resampling landslide samples. A landslide sensitivity model is developed using the LightGBM algorithm, and SHAP is applied to interpret the model and assess the impact of each factor on landslide likelihood. The primary sliding factors at Sijiaying mine include distance from rivers, slope height, profile curvature, rock structure, and distance from faults. Safety thresholds for each factor are determined. This method also provides insights for global and individual slope risk assessment, generating high-risk factor maps to aid in managing and preventing slope instability in open-pit mines.

Suggested Citation

  • Jiang Li & Zhuoying Tan & Naigen Tan & Aboubakar Siddique & Jianshu Liu & Fenglin Wang & Wantao Li, 2025. "Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study," Land, MDPI, vol. 14(4), pages 1-27, March.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:4:p:678-:d:1618516
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    References listed on IDEAS

    as
    1. Dieu Bui & Owe Lofman & Inge Revhaug & Oystein Dick, 2011. "Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression," 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. 59(3), pages 1413-1444, December.
    2. Deliang Sun & Danlu Chen & Jialan Zhang & Changlin Mi & Qingyu Gu & Haijia Wen, 2023. "Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation," Land, MDPI, vol. 12(5), pages 1-37, May.
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