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Enhanced Landslide Risk Assessment Through Non-Probabilistic Stability Analysis: A Hybrid Framework Integrating Space–Time Distribution and Vulnerability Models

Author

Listed:
  • Suxun Shu

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Kang Pi

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Wenhui Gong

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chunmei Zhou

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Jiajun Qian

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Zhiquan Yang

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

Abstract

Landslide risk assessment can quantify the potential damage caused by landslides to disaster-bearing bodies, which can help to reduce casualties and economic losses. It is not only a tool for disaster prevention and mitigation, but also a key step to achieve the coordinated development of the environment, economy, and society, and it provides important support for the realization of the global sustainable development goals (SDGs). In this study, a risk assessment method is proposed for an individual landslide based on the non-probabilistic reliability theory. The method represents an improvement to and innovation in existing risk assessment methods, which can obtain more accurate assessment results with fewer sample data points, refines the methods and steps of landslide risk assessment, and fully considers the destabilization mechanism of the landslide and the interaction with disaster-bearing bodies. A non-probabilistic reliability analysis of the slope was conducted, and the possibility of landslide occurrence was characterized by the failure value of the slope. Moreover, the influence range of the landslide was predicted using empirical formulas; space–time distribution probabilities of the disaster-bearing bodies were estimated by combining their location and activity patterns; and the vulnerability of the disaster-bearing bodies was calculated according to the landslide intensity and the resistance or susceptibility index of the disaster-bearing bodies. The method’s feasibility was verified through its application to the Xiatudiling landslide as a case study. In the process of performing slope stability calculations, it was found that the calculation results of the Monte Carlo method were consistent with those of the non-probabilistic reliability approach proposed in this paper, which was able to obtain more accurate results with less sample data. The personnel life and economic risks were 1.8499 persons/year and CNY 184,858/year (USD 25,448/year), respectively, under heavy rainfall conditions. The results were compared with the risk judgment criteria for geological disasters, and both risk values were unacceptable. After landslide treatment, the possibility of landslide occurrence was reduced, and the personnel life risk and economic risk of the landslide were also reduced. Both risk values then became acceptable. The effect of landslide treatment was obvious. The proposed method provides a new technique for assessing landslide risks and can help in designing mitigation strategies. This method can be applied to landslide risk surveys conducted by geological disaster prevention institutions, demonstrating enhanced applicability in data-scarce regions to improve risk assessment efficiency. It is particularly suitable for emergency management authorities, enabling rapid and comprehensive assessment of landslide risk levels to support informed decision making during critical response scenarios.

Suggested Citation

  • Suxun Shu & Kang Pi & Wenhui Gong & Chunmei Zhou & Jiajun Qian & Zhiquan Yang, 2025. "Enhanced Landslide Risk Assessment Through Non-Probabilistic Stability Analysis: A Hybrid Framework Integrating Space–Time Distribution and Vulnerability Models," Sustainability, MDPI, vol. 17(9), pages 1-23, May.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4146-:d:1648838
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