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Environmental Earth Sciences

, Volume 61, Issue 4, pp 821–836 | Cite as

Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

  • Işık Yilmaz
Original Article

Abstract

This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.

Keywords

Landslide Susceptibility map GIS Conditional probability Logistic regression Artificial neural networks Support vector machine 

Notes

Acknowledgments

Author thanks the two anonymous reviewers for their very constructive and valuable comments which significantly led to the improvement of the paper.

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© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Department of Geological Engineering, Faculty of EngineeringCumhuriyet UniversitySivasTurkey

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