Skip to content
DraconiticResearch
Search Sources API
201,642 studies31 sourcesUpdated 13 min ago
Peer-reviewedCC BYProgress in Disaster Science

Two-stage assessment of regional geological hazard susceptibility under mining disturbance

5 affiliations
  1. 1Faculty of Geography, Yunnan Normal University, Kunming, 650500, China
  2. 2Yunnan Institute of Geology and Mineral Surveying and Mapping Co., Ltd, Kunming, 650051, China; Yunnan Key Laboratory of Intelligent Monitoring and Spatiotemporal Big Data Governance of Natural Resources, Kunming, 650051, China; Corresponding authors at: Yunnan Key Laboratory of Intelligent Monitoring and Spatiotemporal Big Data Governance of Natural Resources, Kunming 650051, China.
  3. 3Yunnan Institute of Geology and Mineral Surveying and Mapping Co., Ltd, Kunming, 650051, China; Yunnan Key Laboratory of Intelligent Monitoring and Spatiotemporal Big Data Governance of Natural Resources, Kunming, 650051, China
  4. 4Faculty of Geography, Yunnan Normal University, Kunming, 650500, China; Yunnan Key Laboratory of Intelligent Monitoring and Spatiotemporal Big Data Governance of Natural Resources, Kunming, 650051, China
  5. 5Faculty of Geography, Yunnan Normal University, Kunming, 650500, China; Yunnan Key Laboratory of Intelligent Monitoring and Spatiotemporal Big Data Governance of Natural Resources, Kunming, 650051, China; Corresponding authors at: Yunnan Key Laboratory of Intelligent Monitoring and Spatiotemporal Big Data Governance of Natural Resources, Kunming 650051, China.

Abstract

Mining disturbs regional environments and triggers geological hazards. Existing studies rely on conventional topographic and geological factors, neglecting quantitative mining disturbance analysis and its evolutionary impact. Disentangling mining's net effect from natural evolution via counterfactual scenarios and revealing its spatial differentiation and distance-decay on susceptibility is the key question. Taking Fuyuan County, Yunnan, as study area, this paper proposes a two-stage framework quantifying mining disturbance impacts on susceptibility through comparative modelling between hypothetical stable and actual growth periods. CA–Markov (1992–2001) projected 2002–2011 counterfactuals for four disturbance factors (FVC, land use, road density, RSEI) absent intensified mining. RF with topographic/geological factors modeled both-period susceptibility. TS-EMD extracted net mining disturbance across both layers, quantifying spatiotemporal patterns. Results: (1) Extremely high susceptibility zones expanded by 368.45 km 2 (an 11.4-percentage-point increase) during the actual growth period, while all other susceptibility zones declined. (2) Fractional Vegetation Cover (FVC), land use, road density and Remote Sensing Ecological Index (RSEI) all greatly affect hazard susceptibility, with RSEI showing the strongest impact. (3) Mining disturbance has distinct spatial heterogeneity and a distance attenuation effect. This framework supports mine disaster prevention, ecological restoration and sustainable mining.

Related

  1. Empirically informed AHP-FR modeling for multi-hazard susceptibility mapping in a mountainous transport corridors
    Muhammad Awais Hussain, Xinyu Xie, Chenxu Su +5
    Scientific ReportsPeer-reviewedCC BY 4.0Full textEnvironmental science & pollutionCrossref
  2. Multi-hazard susceptibility mapping in a karst context using a machine-learning method (MaxEnt)
    Hedieh Soltanpour, Kamal Serrhini, Joel C. Gill +2
    Natural hazards and earth system sciencesPeer-reviewedCC BYFull text8 citedGeology & geochemistryOpenAlex
  3. Machine-learning modelling of regional landslide susceptibility in eThekwini, South Africa
    Cher Petersen, Paidamwoyo Mhangara, Laven Naidoo +1
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textEnvironmental science & pollutionCrossref
  4. Chemical Alternatives Assessment: Progress and Perspectives in Hazard Characterization, Exposure Assessment, and Life Cycle Assessment
    Yipeng Lu, Xiaoqian Chen, Pin Gao
    Journal of xenobioticsPeer-reviewedCC BYFull textEnvironmental science & pollutionEurope PMC
  5. Multi-scenario probabilistic hazard assessment of flash flood disasters in Chengkou County based on a multi-state system model
    Lin Xiaosong, Zhang Congxuan, Wang Meili +2
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textClimate scienceCrossref
BibTeX
@article{chen2026twostage,
  title = {{Two-stage assessment of regional geological hazard susceptibility under mining disturbance}},
  author = {Jufang Chen and Yamei Feng and Shaowen Cao and Wenbin Xie and Jia Li and Ping Duan},
  journal = {Progress in Disaster Science},
  year = {2026},
  month = oct,
  doi = {10.1016/j.pdisas.2026.100759},
  url = {https://doi.org/10.1016/j.pdisas.2026.100759},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Two-stage assessment of regional geological hazard susceptibility under mining disturbance
AU  - Jufang Chen
AU  - Yamei Feng
AU  - Shaowen Cao
AU  - Wenbin Xie
AU  - Jia Li
AU  - Ping Duan
PY  - 2026
DA  - 2026/10/30
JO  - Progress in Disaster Science
DO  - 10.1016/j.pdisas.2026.100759
UR  - https://doi.org/10.1016/j.pdisas.2026.100759
AB  - Mining disturbs regional environments and triggers geological hazards. Existing studies rely on conventional topographic and geological factors, neglecting quantitative mining disturbance analysis and its evolutionary impact. Disentangling mining's net effect from natural evolution via counterfactual scenarios and revealing its spatial differentiation and distance-decay on susceptibility is the key question. Taking Fuyuan County, Yunnan, as study area, this paper proposes a two-stage framework quantifying mining disturbance impacts on susceptibility through comparative modelling between hypothetical stable and actual growth periods. CA–Markov (1992–2001) projected 2002–2011 counterfactuals for four disturbance factors (FVC, land use, road density, RSEI) absent intensified mining. RF with topographic/geological factors modeled both-period susceptibility. TS-EMD extracted net mining disturbance across both layers, quantifying spatiotemporal patterns. Results: (1) Extremely high susceptibility zones expanded by 368.45 km 2 (an 11.4-percentage-point increase) during the actual growth period, while all other susceptibility zones declined. (2) Fractional Vegetation Cover (FVC), land use, road density and Remote Sensing Ecological Index (RSEI) all greatly affect hazard susceptibility, with RSEI showing the strongest impact. (3) Mining disturbance has distinct spatial heterogeneity and a distance attenuation effect. This framework supports mine disaster prevention, ecological restoration and sustainable mining.
SN  - 2590-0617
ER  -