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Abstract

This study compares the predictive performance of GIS-based landslide susceptibility mapping (LSM) using four different kernel functions in support vector machines (SVMs). Nine possible causal criteria were considered based on earlier similar studies for an area in the eastern part of the Khuzestan province of southern Iran. Different models and the resulting landslide susceptibility maps were created using information on known landslide events from a landslide inventory dataset. The models were trained using landslide inventory dataset. A two-step accuracy assessment was implemented to validate the results and to compare the capability of each function. The radial basis function was identified as the most efficient kernel function for LSM with the resulting landslide susceptibility map showing the highest predictive accuracy, followed by the polynomial kernel function. According to the obtained results, it concluded that using SVMs can generally be considered to be an effective method for LSM while it demands careful consideration of kernel function. The results of the present research will also assist other researchers to select the best SVM kernel function to use for LSM.

Item Type:	Article
Authors/Creators:	Feizizadeh, B and Roodposhti, MS and Blaschke, T and Aryal, J
Keywords:	kernel function, landslide susceptibility mapping, support vector machine, southern Izeh
Journal or Publication Title:	Arabian Journal of Geosciences
Publisher:	Springer
ISSN:	1866-7511
DOI / ID Number:	https://doi.org/10.1007/s12517-017-2918-z
Copyright Information:	Copyright 2017 Saudi Society for Geosciences
Related URLs:	Google Scholar: Aryal, J UTAS Profile: Aryal, J
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