Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers

Al-Bawi, AJ; Al-Abadi, AM; Pradhan, B; Alamri, AM

Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers

Al-Bawi, AJ Al-Abadi, AM Pradhan, B

Alamri, AM

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Publisher:: Taylor & Francis Open Access
Publication Type:: Journal Article
Citation:: Geomatics, Natural Hazards and Risk, 2021, 12, (1), pp. 3035-3062
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Al-Bawi, AJ
dc.contributor.author	Al-Abadi, AM
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Alamri, AM
dc.date.accessioned	2022-02-17T05:17:46Z
dc.date.available	2022-02-17T05:17:46Z
dc.date.issued	2021-01-01
dc.identifier.citation	Geomatics, Natural Hazards and Risk, 2021, 12, (1), pp. 3035-3062
dc.identifier.issn	1947-5705
dc.identifier.issn	1947-5713
dc.identifier.uri	http://hdl.handle.net/10453/154648
dc.description.abstract	Gully erosion is an erosive process that contributes considerably to the shape of the earth’s surface and is a major contributor to land degradation and soil loss. This study applied a methodology for mapping gully erosion susceptibility using only topographic related attributes derived from a medium-resolution digital elevation model (DEM) and a hybrid analytical hierarchy process (AHP) and the technique for an order of preference by similarity to ideal solutions (TOPSIS) and compare the results with naïve Bayes (NB) and support vector machine learning (SVM) algorithms. A transboundary sub-basin in an arid area of southern Iraq was selected as a case study. The performance of the developed models was compared using the receiver operating characteristic curve (ROC). Results showed that the areas under the ROC were 0.933, 0.936, and 0.955 for AHP-TOPSIS, NB, and SVM with radial basis function, respectively, which indicated that the performance of simply derived AHP-TOPSIS model is similar to sophisticated NB and SVM models. Findings indicated that a medium resolution DEM and AHP-TOPSIS are a promising tool for mapping of gully erosion susceptibility.
dc.language	en
dc.publisher	Taylor & Francis Open Access
dc.relation.ispartof	Geomatics, Natural Hazards and Risk
dc.relation.isbasedon	10.1080/19475705.2021.1994024
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0406 Physical Geography and Environmental Geoscience
dc.title	Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0406 Physical Geography and Environmental Geoscience
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	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-17T05:17:43Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	1

Abstract:

Gully erosion is an erosive process that contributes considerably to the shape of the earth’s surface and is a major contributor to land degradation and soil loss. This study applied a methodology for mapping gully erosion susceptibility using only topographic related attributes derived from a medium-resolution digital elevation model (DEM) and a hybrid analytical hierarchy process (AHP) and the technique for an order of preference by similarity to ideal solutions (TOPSIS) and compare the results with naïve Bayes (NB) and support vector machine learning (SVM) algorithms. A transboundary sub-basin in an arid area of southern Iraq was selected as a case study. The performance of the developed models was compared using the receiver operating characteristic curve (ROC). Results showed that the areas under the ROC were 0.933, 0.936, and 0.955 for AHP-TOPSIS, NB, and SVM with radial basis function, respectively, which indicated that the performance of simply derived AHP-TOPSIS model is similar to sophisticated NB and SVM models. Findings indicated that a medium resolution DEM and AHP-TOPSIS are a promising tool for mapping of gully erosion susceptibility.

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http://hdl.handle.net/10453/154648