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

Li, Jiang; Tan, Zhuoying; Tan, Naigen; Siddique, Aboubakar; Liu, Jianshu; Wang, Fenglin; Li, Wantao

doi:10.3390/land14040678

Open AccessArticle

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

by

Jiang Li

¹,

Zhuoying Tan

^1,*,

Naigen Tan

¹,

Aboubakar Siddique

¹

,

Jianshu Liu

¹,

Fenglin Wang

² and

Wantao Li

²

¹

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

²

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

^*

Author to whom correspondence should be addressed.

Land 2025, 14(4), 678; https://doi.org/10.3390/land14040678

Submission received: 15 January 2025 / Revised: 11 March 2025 / Accepted: 17 March 2025 / Published: 23 March 2025

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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.

Keywords: open pit; landslide susceptibility; interpretable machine learning; LightGBM; SHAP

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MDPI and ACS Style

Li, J.; Tan, Z.; Tan, N.; Siddique, A.; Liu, J.; Wang, F.; Li, W. Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study. Land 2025, 14, 678. https://doi.org/10.3390/land14040678

AMA Style

Li J, Tan Z, Tan N, Siddique A, Liu J, Wang F, Li W. Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study. Land. 2025; 14(4):678. https://doi.org/10.3390/land14040678

Chicago/Turabian Style

Li, Jiang, Zhuoying Tan, Naigen Tan, Aboubakar Siddique, Jianshu Liu, Fenglin Wang, and Wantao Li. 2025. "Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study" Land 14, no. 4: 678. https://doi.org/10.3390/land14040678

APA Style

Li, J., Tan, Z., Tan, N., Siddique, A., Liu, J., Wang, F., & Li, W. (2025). Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study. Land, 14(4), 678. https://doi.org/10.3390/land14040678

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

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