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Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study

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Abstract

The crucial and difficult task in landslide susceptibility analysis is estimating the probability of occurrence of future landslides in a study area under a specific set of geomorphic and topographic conditions. This task is addressed with a data-driven probabilistic model using likelihood ratio or frequency ratio and is applied to assess the occurrence of landslides in the Tevankarai Ar sub-watershed, Kodaikkanal, South India. The landslides in the study area are triggered by heavy rainfall. Landslide-related factors—relief, slope, aspect, plan curvature, profile curvature, land use, soil, and topographic wetness index proximity to roads and proximity to lineaments—are considered for the study. A geospatial database of the related landslide factors is constructed using Arcmap in GIS environment. Landslide inventory of the area is produced by detailed field investigation and analysis of the topographical maps. The results are validated using temporal data of known landslide locations. The area under the curve shows that the accuracy of the model is 85.83%. In the reclassified final landslide susceptibility map, 14.48% of the area is critical in nature, falling under the very high hazard zone, and 67.86% of the total validation dataset landslides fall in this zone. This landslide susceptibility map is a vital tool for town planning, land use, and land cover planning and to reduce risks caused by landslides.

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Authors and Affiliations

  1. SASTRA University, Thanjavur, Tamil Nadu, India

    E. Ramani Sujatha, Victor Rajamanickam, P. Kumaravel & E. Saranathan

Authors
  1. E. Ramani Sujatha
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  2. Victor Rajamanickam
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  3. P. Kumaravel
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  4. E. Saranathan
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Correspondence to E. Ramani Sujatha.

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Sujatha, E.R., Rajamanickam, V., Kumaravel, P. et al. Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study. Arab J Geosci 6, 429–440 (2013). https://doi.org/10.1007/s12517-011-0356-x

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  • DOI: https://doi.org/10.1007/s12517-011-0356-x

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