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:	Refereed Article
Keywords:	kernel function, landslide susceptibility mapping, support vector machine, southern Izeh
Research Division:	Engineering
Research Group:	Geomatic engineering
Research Field:	Photogrammetry and remote sensing
Objective Division:	Environmental Management
Objective Group:	Other environmental management
Objective Field:	Other environmental management not elsewhere classified
UTAS Author:	Roodposhti, MS (Mr Majid Roodposhti)
UTAS Author:	Aryal, J (Dr Jagannath Aryal)
ID Code:	115366
Year Published:	2017
Web of Science® Times Cited:	71
Deposited By:	Geography and Spatial Science
Deposited On:	2017-03-20
Last Modified:	2022-08-23
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