A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison

Althuwaynee, OF; Pradhan, B; Lee, S

A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison

Althuwaynee, OF Pradhan, B

Lee, S

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Publisher:: Taylor & Francis
Publication Type:: Journal Article
Citation:: International Journal of Remote Sensing, 2016, 37, (5), pp. 1190-1209
Issue Date:: 2016

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Field	Value	Language
dc.contributor.author	Althuwaynee, OF
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Lee, S
dc.date.accessioned	2022-07-14T01:02:15Z
dc.date.available	2022-07-14T01:02:15Z
dc.date.issued	2016
dc.identifier.citation	International Journal of Remote Sensing, 2016, 37, (5), pp. 1190-1209
dc.identifier.issn	0143-1161
dc.identifier.issn	1366-5901
dc.identifier.uri	http://hdl.handle.net/10453/158874
dc.description.abstract	This article uses an integrated methodology based on a chi-squared automatic interaction detection (CHAID) model combined with analytic hierarchy process (AHP) for pair-wise comparison to assess medium-scale landslide susceptibility in a catchment in the Inje region of South Korea. An inventory of 3596 landslide locations was collected using remote sensing, and a random sample comprising 30% of these was used to validate the model. The remaining portion (70%) was processed by the nearest-neighbour index (NNI) technique and used for extracting the cluster patterns at each location. These data were used for model training purposes. Ten landslide-conditioning factors (independent variables) representing four main domains, namely (1) topology, (2) geology, (3) hydrology, and (4) land cover, were used to produce two landslide-susceptibility maps. The first landslide-susceptibility map (LSM1) was produced by overlaying the terminal nodes of the CHAID result tree. The second landslide-susceptibility map (LSM2) was produced using the overlay result of AHP pair-wise comparisons of CHAID terminal nodes. The prediction rate curve results were better with LSM2 (area under the prediction curve (AUC) = 0.80) than with LSM1 (AUC = 0.76). The results confirmed that the integrated hybrid model has superior prediction performance and reliability, and it is recommended for future use in medium-scale landslide-susceptibility mapping.
dc.language	English
dc.publisher	Taylor & Francis
dc.relation.ispartof	International Journal of Remote Sensing
dc.relation.isbasedon	10.1080/01431161.2016.1148282
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-14T01:02:13Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	5

Abstract:

This article uses an integrated methodology based on a chi-squared automatic interaction detection (CHAID) model combined with analytic hierarchy process (AHP) for pair-wise comparison to assess medium-scale landslide susceptibility in a catchment in the Inje region of South Korea. An inventory of 3596 landslide locations was collected using remote sensing, and a random sample comprising 30% of these was used to validate the model. The remaining portion (70%) was processed by the nearest-neighbour index (NNI) technique and used for extracting the cluster patterns at each location. These data were used for model training purposes. Ten landslide-conditioning factors (independent variables) representing four main domains, namely (1) topology, (2) geology, (3) hydrology, and (4) land cover, were used to produce two landslide-susceptibility maps. The first landslide-susceptibility map (LSM1) was produced by overlaying the terminal nodes of the CHAID result tree. The second landslide-susceptibility map (LSM2) was produced using the overlay result of AHP pair-wise comparisons of CHAID terminal nodes. The prediction rate curve results were better with LSM2 (area under the prediction curve (AUC) = 0.80) than with LSM1 (AUC = 0.76). The results confirmed that the integrated hybrid model has superior prediction performance and reliability, and it is recommended for future use in medium-scale landslide-susceptibility mapping.

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http://hdl.handle.net/10453/158874