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Geosciences 2016, 6(1), 14; doi:10.3390/geosciences6010014

Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale

1
Department of Geography, Harokopio University, El. Venizelou 70, Athens 17671, Greece
2
Department of Geological Sciences, University of KwaZulu-Natal, Westville, Private Bag X54001, South Africa
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Ruiliang Pu and Jesus Martinez-Frias
Received: 21 December 2015 / Revised: 8 February 2016 / Accepted: 24 February 2016 / Published: 1 March 2016
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Geomorphological Mapping)
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Abstract

In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak Ground Acceleration (PGA)—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC) analysis depending on an independent (validation) dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale. View Full-Text
Keywords: integration modeling; landslide susceptibility; expert-based fuzzy weighting; bivariate statistics; GIS; Greece integration modeling; landslide susceptibility; expert-based fuzzy weighting; bivariate statistics; GIS; Greece
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chalkias, C.; Polykretis, C.; Ferentinou, M.; Karymbalis, E. Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale. Geosciences 2016, 6, 14.

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