Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study
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 oc...
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MDPI AG
2025-03-01
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| author | Jiang Li Zhuoying Tan Naigen Tan Aboubakar Siddique Jianshu Liu Fenglin Wang Wantao Li |
| author_facet | Jiang Li Zhuoying Tan Naigen Tan Aboubakar Siddique Jianshu Liu Fenglin Wang Wantao Li |
| author_sort | Jiang Li |
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| description | 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. |
| format | Article |
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| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-03-01 |
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| series | Land |
| spelling | doaj-art-a8a9fccabb42437bb9e704f1925fd99d2025-08-20T03:13:47ZengMDPI AGLand2073-445X2025-03-0114467810.3390/land14040678Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case StudyJiang Li0Zhuoying Tan1Naigen Tan2Aboubakar Siddique3Jianshu Liu4Fenglin Wang5Wantao Li6School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaHebei Iron & Steel Group, Luanxian Sijiaying Iron Mine Co., Ltd., Tangshan 063700, ChinaHebei Iron & Steel Group, Luanxian Sijiaying Iron Mine Co., Ltd., Tangshan 063700, ChinaSlope 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.https://www.mdpi.com/2073-445X/14/4/678open pitlandslide susceptibilityinterpretable machine learningLightGBMSHAP |
| spellingShingle | Jiang Li Zhuoying Tan Naigen Tan Aboubakar Siddique Jianshu Liu Fenglin Wang Wantao Li Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study Land open pit landslide susceptibility interpretable machine learning LightGBM SHAP |
| title | Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study |
| title_full | Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study |
| title_fullStr | Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study |
| title_full_unstemmed | Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study |
| title_short | Machine Learning Method Application to Detect Predisposing Factors to Open-Pit Landslides: The Sijiaying Iron Mine Case Study |
| title_sort | machine learning method application to detect predisposing factors to open pit landslides the sijiaying iron mine case study |
| topic | open pit landslide susceptibility interpretable machine learning LightGBM SHAP |
| url | https://www.mdpi.com/2073-445X/14/4/678 |
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