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Landslide Risk Mapping Using the Weight-of-Evidence Method in the Datong Mining Area, Qinghai Province

Author

Listed:
  • He Yang

    (College of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China)

  • Qihong Wu

    (College of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China)

  • Jianhui Dong

    (College of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
    Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road, Traffic Safety of Ministry of Education, Changsha University of Science & Technology, Changsha 410114, China)

  • Feihong Xie

    (College of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China)

  • Qixue Zhang

    (College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China)

Abstract

Qinghai is rich in mineral resources, but frequent and large-scale mineral mining has caused secondary damage to the fragile primary surface and produced a large number of landslide disasters. In complex geological environments such as glacier ablation and frequent tectonic movements, a complete quantitative evaluation method for landslide risk in high-cold mining areas has not yet been formed. In view of this, this article uses the field survey and remote sensing data of the Datong mining area in Qinghai Province in 2012 as the basic data. We comprehensively considered five first-level factors (13 s-level factors) including topography, lithological structure, mining engineering activities, land use, and dynamic deformation as evaluation indicators for landslide susceptibility in mining areas, and used the Topographic Wetness Index (TWI) and the Human Engineering Activity Intensity (HEAI) to quantitatively estimate the hazard of landslide according to the landslide trigger mechanism. The weight-of-evidence approach was used for landslide hazard and risk mapping under different landslide--inducing conditions. The results indicate that the extremely high-hazard areas induced by human engineering activities account for 14% of the total area, and the extremely high-risk areas account for 13% of the total area in the Datong mining area, and the area of the extremely high-risk area is large; the landslide risk assessment mapping model constructed in this study can effectively evaluate the probability of slope instability caused by rainfall and human engineering activities. The effective value of the receiver operating characteristic (ROC) curve of the sensitivity assessment model reaches 0.863, and the evaluation results are consistent with reality; using the weight-of-evidence model for landslide risk assessment is more in line with the actual situation in alpine mining areas, and is more suitable for guiding landslide risk management and disaster prevention and mitigation in mining areas.

Suggested Citation

  • He Yang & Qihong Wu & Jianhui Dong & Feihong Xie & Qixue Zhang, 2023. "Landslide Risk Mapping Using the Weight-of-Evidence Method in the Datong Mining Area, Qinghai Province," Sustainability, MDPI, vol. 15(14), pages 1-27, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11330-:d:1198794
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    References listed on IDEAS

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    3. Li Zhuo & Yupu Huang & Jing Zheng & Jingjing Cao & Donghu Guo, 2023. "Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance," Sustainability, MDPI, vol. 15(11), pages 1-23, June.
    4. Haoran Fang & Yun Shao & Chou Xie & Bangsen Tian & Chaoyong Shen & Yu Zhu & Yihong Guo & Ying Yang & Guanwen Chen & Ming Zhang, 2023. "A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
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    Cited by:

    1. Ruixia Ma & Yan Lyu & Tianbao Chen & Qian Zhang, 2023. "Preliminary Risk Assessment of Geological Disasters in Qinglong Gorge Scenic Area of Taihang Mountain with GIS Based on Analytic Hierarchy Process and Logistic Regression Model," Sustainability, MDPI, vol. 15(22), pages 1-19, November.

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