(Article of periodic en Anglais - 2014)

Document title

A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping

Authors(s) and Affiliation(s)

ALTHUWAYNEE O.F. (1) ; PRADHAN B. (1) ; PARK H.-J. (2) ; LEE J.H. (2) ;
(1) Dept. of Civil Engineering, Fac. of Civil Engineering, Univ., Serdang, MALAISIE
(2) Dept. of Geoinformation Engineering, Sejong Univ., Seoul, COREE, REPUBLIQUE DE


This study compares the landslide susceptibility maps from 4 application models : 1) the bivariate model of the Dempster–Shafer based evidential belief function (EBF); 2) integration of the EBF in the knowledge-based analytical hierarchy process (AHP) as a pairwise comparison model processed by using all available causative factors; 3) integration of the EBF in the knowledge-based AHP as a pairwise comparison model by using high nominated causative factor weights only; and 4) integrated EBF in the logistic regression (LR) as a multivariate model by using nominated causative factor weights only. These models were tested in Pohang and Gyeongju Cities (South Korea) by using the geographic information system GIS platform. Models 1 and 3 show better performance than LR. These resultant maps can be used to extend the capability of bivariate statistical based model, by finding the relationship between each single conditioning factor and landslide locations, moreover, the proposed ensemble model can be used to show the inter-relationships importance between each conditioning factors, without the need to refer to the multivariate statistic


Article of periodic

published at : Catena / ISSN 0341-8162

Editor : Catena, Cremlingen-Destedt - ALLEMAGNE (1973)

Millesime : 2014, vol. 114 [pp. 21-36]

Bibliographic references : 1 p.

Collation : 9 tabl., 6 fig.



INIST-CNRS, Cote INIST : 16767

Digital Object Identifier

Go to electronic document thanks to its DOI : doi:10.1016/j.catena.2013.10.011

Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI), 2014
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