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An Improved Unascertained Measure-Set Pair Analysis Model Based on Fuzzy AHP and Entropy for Landslide Susceptibility Zonation Mapping

Author

Listed:
  • Xiaojie Yang

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
    School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Zhenli Hao

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
    School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Keyuan Liu

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
    School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Zhigang Tao

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
    School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China)

  • Guangcheng Shi

    (State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology, Beijing 100083, China
    School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing 100083, China)

Abstract

Landslides are one of the most destructive and common geological disasters in the Tonglvshan mining area, which seriously threatens the safety of surrounding residents and the Tonglvshan ancient copper mine site. Therefore, to effectively reduce the landslide risk and protect the safety of the Tonglvshan ancient copper mine site, it is necessary to carry out a systematic assessment of the landslide susceptibility in the study area. Combining the unascertained measure (UM) theory, the dynamic comprehensive weighting (DCW) method based on the fuzzy analytic hierarchy process (AHP)-entropy weight method and the set pair analysis (SPA) theory, an improved UM-SPA coupling model for landslide susceptibility assessment is proposed in this study. First, a hierarchical evaluation index system including 10 landslide conditioning factors is constructed. Then, the dynamic comprehensive weighting method based on the fuzzy AHP-entropy weight method is used to assign independent comprehensive weights to each evaluation unit. Finally, we optimize the credible degree recognition criteria of UM theory by introducing SPA theory to quantitatively determine the landslide susceptibility level. The results show that the improved UM-SPA model can produce landslide susceptibility zoning maps with high reliability. The whole study area is divided into five susceptibility levels. 5.8% and 10.16% of the Tonglvshan mining area are divided into extremely high susceptibility areas and high susceptibility areas, respectively. The low and extremely low susceptibility areas account for 30.87% and 34.14% of the total area of the study area, respectively. Comparison with the AHP and Entropy-FAHP models indicates that the improved UM-SPA model (AUC = 0.777) shows a better performance than the Entropy-FAHP models (AUC = 0.764) and the conventional AHP (AUC = 0.698). Therefore, these results can provide reference for emergency planning, disaster reduction and prevention decision-making in the Tonglvshan mining area.

Suggested Citation

  • Xiaojie Yang & Zhenli Hao & Keyuan Liu & Zhigang Tao & Guangcheng Shi, 2023. "An Improved Unascertained Measure-Set Pair Analysis Model Based on Fuzzy AHP and Entropy for Landslide Susceptibility Zonation Mapping," Sustainability, MDPI, vol. 15(7), pages 1-28, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6205-:d:1115778
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    1. Peng Yu & Jie Dong & Hongwei Hao & Yongjian Xie & Hui Zhang & Jianshou Wang & Chenghao Zhu & Yong Guan & Haochen Yu, 2023. "Risk Assessment and Prevention Planning for Collapse Geological Hazards Considering Extreme Rainfall—A Case Study of Laoshan District in Eastern China," Land, MDPI, vol. 12(8), pages 1-22, August.
    2. Saeed Alqadhi & Javed Mallick & Meshel Alkahtani & Intikhab Ahmad & Dhafer Alqahtani & Hoang Thi Hang, 2024. "Developing a hybrid deep learning model with explainable artificial intelligence (XAI) for enhanced landslide susceptibility modeling and management," 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. 120(4), pages 3719-3747, March.
    3. Yuqiang He & Ziyan Bin & Xiaolei Xu & Hongsheng Yu & Yan Zhang & Na Li & Man Li, 2025. "Landslide Risk Assessment Along Railway Lines Using Multi-Source Data: A GameTheory-Based Integrated Weighting Approach for Sustainable Infrastructure Planning," Sustainability, MDPI, vol. 17(12), pages 1-18, June.
    4. Daming Lin & Yufang Zhang & Shumao Qiu & Mingzhou Bai & Haoying Xia & Wei Qiao & Zhenyu Tang, 2024. "Landslide Susceptibility Assessment Method during the Construction of Highways Based on the Index Complexity Algorithm," Sustainability, MDPI, vol. 16(14), pages 1-14, July.
    5. Xiaoning Zhao & Zhongcheng Wei & Yukun Gao & Penggang Yin, 2023. "Laboratory Risk Assessment Based on SHELL-HACCP-Cloud Model," Sustainability, MDPI, vol. 15(24), pages 1-18, December.

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