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Background/Objective <p>Traditional landslide susceptibility assessment methods, such as the Slope Stability Susceptibility Evaluation Parameter (SSEP), are often constrained by subjective judgments. This study aims to develop and validate a novel integrated approach that combines the SSEP framework with Geographic Information System (GIS)-based bi-variate statistical analysis to enhance objectivity and reliability in landslide susceptibility mapping.</p> Methods <p>The study was conducted in the landslide-prone Shoko district of Southwestern Ethiopia. Ten causative factors—slope angle, relative relief, land use/land cover, rainfall, seismic activity, soil type, rock type, structural discontinuities, groundwater traces, and human development activities—were analyzed using field surveys, SPOT5 satellite imagery, and ASTER DEM data. A detailed landslide inventory map documenting 14 historical events was created through fieldwork and remote sensing analysis (satellite imagery and Google Earth). Bi-variate statistics were applied to calculate hazard indexes, which were then used to objectively calibrate and refine the conventional SSEP ratings.</p> Results <p>The integrated methodology produced a landslide susceptibility map classifying the area into four distinct zones: low, moderate, high, and very high susceptibility. Validation against the known inventory demonstrated the model's effectiveness, with 78.57% (11 out of 14) of the recorded landslides located within the high and very high susceptibility zones.</p> Conclusion <p>The integration of SSEP with bi-variate statistical analysis successfully reduces the inherent subjectivity of heuristic models. The resulting susceptibility map shows high predictive accuracy, confirming the reliability of the proposed hybrid approach. This output provides a vital tool for informed land-use planning and proactive hazard mitigation strategies in the study area and similar environments.</p>

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Reducing subjectivity in landslide susceptibility mapping method: an integrated methodology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月9日

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  • Searom Gebremicheal Gebru,
  • Tegene Wubshet Shewangzaw

摘要

Background/Objective

Traditional landslide susceptibility assessment methods, such as the Slope Stability Susceptibility Evaluation Parameter (SSEP), are often constrained by subjective judgments. This study aims to develop and validate a novel integrated approach that combines the SSEP framework with Geographic Information System (GIS)-based bi-variate statistical analysis to enhance objectivity and reliability in landslide susceptibility mapping.

Methods

The study was conducted in the landslide-prone Shoko district of Southwestern Ethiopia. Ten causative factors—slope angle, relative relief, land use/land cover, rainfall, seismic activity, soil type, rock type, structural discontinuities, groundwater traces, and human development activities—were analyzed using field surveys, SPOT5 satellite imagery, and ASTER DEM data. A detailed landslide inventory map documenting 14 historical events was created through fieldwork and remote sensing analysis (satellite imagery and Google Earth). Bi-variate statistics were applied to calculate hazard indexes, which were then used to objectively calibrate and refine the conventional SSEP ratings.

Results

The integrated methodology produced a landslide susceptibility map classifying the area into four distinct zones: low, moderate, high, and very high susceptibility. Validation against the known inventory demonstrated the model's effectiveness, with 78.57% (11 out of 14) of the recorded landslides located within the high and very high susceptibility zones.

Conclusion

The integration of SSEP with bi-variate statistical analysis successfully reduces the inherent subjectivity of heuristic models. The resulting susceptibility map shows high predictive accuracy, confirming the reliability of the proposed hybrid approach. This output provides a vital tool for informed land-use planning and proactive hazard mitigation strategies in the study area and similar environments.