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Comparative analysis of slope stability factors and a hydrological dataset for landslide assessment

Author

Listed:
  • Youngseok Song

    (Konkuk University)

  • Wooyoung Na

    (Dong-A University)

  • Changhyun Jun

    (Korea University)

  • Sang Yeob Kim

    (Konkuk University)

Abstract

In slope stability analysis, infinite slope stability has been widely assessed using field data; however, recently, remote sensing techniques have been developed to determine the probability of slope failure. This study aims to verify the suitability of a hydrological dataset for accurate slope stability analysis by comparing it with conventional factors employed in the infinite slope stability model. Sensitivity analysis of the factors associated with infinite slope stability revealed that hydraulic conductivity and cohesion, which are related to the wetness index, played particularly crucial roles. Thus, data for the volumetric soil water in different layers, total precipitation, and runoff taken from the ERA5-Land dataset were selected as a comparison. Analysis of the relationships between the wetness index, the factor of safety, and variables from ERA5-Land found that total precipitation considering runoff produced a trend similar to infinite slope stability analysis using field data. Therefore, the present study confirms that a hydrological dataset can be employed to estimate slope stability when field data are unavailable or unreliable. In addition, the hydrological dataset can also cover a larger target slope area during slope stability monitoring and failure prediction.

Suggested Citation

  • Youngseok Song & Wooyoung Na & Changhyun Jun & Sang Yeob Kim, 2025. "Comparative analysis of slope stability factors and a hydrological dataset for landslide assessment," 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. 121(4), pages 3981-3996, March.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:4:d:10.1007_s11069-024-06964-9
    DOI: 10.1007/s11069-024-06964-9
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    References listed on IDEAS

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    1. Ahmed M. Youssef & Bosy A. El‑Haddad & Hariklia D. Skilodimou & George D. Bathrellos & Foroogh Golkar & Hamid Reza Pourghasemi, 2024. "Landslide susceptibility, ensemble machine learning, and accuracy methods in the southern Sinai Peninsula, Egypt: Assessment and Mapping," 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(15), pages 14227-14258, December.
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