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A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model

Author

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  • Aihua Wei

    (Hebei GEO University
    Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources
    Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure)

  • Duo Li

    (Hebei GEO University
    Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources
    Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure)

  • Yahong Zhou

    (Hebei GEO University
    Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources
    Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure)

  • Qinghai Deng

    (Shandong University of Science and Technology)

  • Liangdong Yan

    (Hebei GEO University
    Hebei Province Key Laboratory of Sustained Utilization and Development of Water Resources
    Hebei Province Collaborative Innovation Center for Sustainable Utilization of Water Resources and Optimization of Industrial Structure)

Abstract

The evolution of cover collapse is a severe hazard in karst regions. The main objective of the present work was to develop a novel approach that combined both subjective and objective methodologies to evaluate sinkhole susceptibility. Based on the comprehensive analysis of the mechanisms for sinkholes, a typical subjective method was first built using the analytic hierarchy process (AHP) with a hierarchical structure that included nine factors. Considering the apparent disadvantage of AHP, the catastrophe theory was integrated to determine the weight of the criterion factors. To further improve and avoid the bias of the assignment of weights, the entropy method was then integrated into the model to objectively and reasonably determine the order of the index factors and weights of the sub-factors in the index layer during the calculation of the catastrophe model. The verification results showed that the combination of the subjective and objective approaches was indeed suitable to indicate collapse susceptibility. The sensitivity analysis results indicated that the thickness of the overlying layer and karst development were the most sensitive parameters, as indicated by the high rate value using the subjective method. The karst collapse area was then classified into very high-, high-, medium-, and low-susceptibility areas, which accounted for 20.09%, 19.82%, 38.58%, and 21.51% of the total area in the study region. The extraction of groundwater, especially mine draining, was the most important factor, causing more severe hazards, especially in the very high- and high-susceptibility areas.

Suggested Citation

  • Aihua Wei & Duo Li & Yahong Zhou & Qinghai Deng & Liangdong Yan, 2021. "A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model," 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. 105(1), pages 405-430, January.
  • Handle: RePEc:spr:nathaz:v:105:y:2021:i:1:d:10.1007_s11069-020-04317-w
    DOI: 10.1007/s11069-020-04317-w
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    2. Mohammad Mehrabi, 2022. "Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy," 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. 111(1), pages 901-937, March.
    3. Zewei Zhang & Qingjie Qi & Ye Cheng & Dawei Cui & Jinghu Yang, 2024. "An Integrated Model for Risk Assessment of Urban Road Collapse Based on China Accident Data," Sustainability, MDPI, vol. 16(5), pages 1-17, March.
    4. Xiaoyi Zhang & Yichen Ruan & Weihao Xuan & Haijun Bao & Zhenhong Du, 2023. "Risk assessment and spatial regulation on urban ground collapse based on geo-detector: a case study of Hangzhou urban area," 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. 118(1), pages 525-543, August.

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