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Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping

Citation

Feizizadeh, B and Roodposhti, MS and Blaschke, T and Aryal, J, Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping, Arabian Journal of Geosciences, 10, (5) Article 122. ISSN 1866-7511 (2017) [Refereed Article]

Copyright Statement

Copyright 2017 Saudi Society for Geosciences

DOI: doi:10.1007/s12517-017-2918-z

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 Details

Item Type:Refereed Article
Keywords:kernel function, landslide susceptibility mapping, support vector machine
Research Division:Engineering
Research Group:Geomatic Engineering
Research Field:Photogrammetry and Remote Sensing
Objective Division:Environment
Objective Group:Environmental and Natural Resource Evaluation
Objective Field:Environmental Management Systems
Author:Roodposhti, MS (Mr Majid Roodposhti)
Author:Aryal, J (Dr Jagannath Aryal)
ID Code:115366
Year Published:2017
Deposited By:Geography and Spatial Science
Deposited On:2017-03-20
Last Modified:2017-04-12
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