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Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression

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Abstract

Identification of landslides and production of landslide susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning. Accuracy of the landslide susceptibility maps is important for reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area.

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Authors and Affiliations

  1. Department of Geodetic and Photogrammetric Engineering, Gebze Institute of Technology, Cayirova Campus, 41400, Gebze-Kocaeli, Turkey

    Taskin Kavzoglu, Emrehan Kutlug Sahin & Ismail Colkesen

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  1. Taskin Kavzoglu
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  2. Emrehan Kutlug Sahin
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  3. Ismail Colkesen
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Correspondence to Taskin Kavzoglu.

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Kavzoglu, T., Sahin, E.K. & Colkesen, I. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11, 425–439 (2014). https://doi.org/10.1007/s10346-013-0391-7

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  • DOI: https://doi.org/10.1007/s10346-013-0391-7

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