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

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 mediu...

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Autores principales:	Ahmed J. Al-Bawi, Alaa M. Al-Abadi, Biswajeet Pradhan, Abdullah M. Alamri
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	gully erosion gullies gis remote sensing mcdm topsis iraq Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Risk in industry. Risk management HD61
Acceso en línea:	https://doaj.org/article/a4f3cee1e20f41c5979b521a89d6b7af
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spelling	oai:doaj.org-article:a4f3cee1e20f41c5979b521a89d6b7af2021-11-04T15:00:42ZAssessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers1947-57051947-571310.1080/19475705.2021.1994024https://doaj.org/article/a4f3cee1e20f41c5979b521a89d6b7af2021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/19475705.2021.1994024https://doaj.org/toc/1947-5705https://doaj.org/toc/1947-5713Gully 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.Ahmed J. Al-BawiAlaa M. Al-AbadiBiswajeet PradhanAbdullah M. AlamriTaylor & Francis Grouparticlegully erosiongulliesgisremote sensingmcdmtopsisiraqEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350Risk in industry. Risk managementHD61ENGeomatics, Natural Hazards & Risk, Vol 12, Iss 1, Pp 3035-3062 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gully erosion gullies gis remote sensing mcdm topsis iraq Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Risk in industry. Risk management HD61
spellingShingle	gully erosion gullies gis remote sensing mcdm topsis iraq Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Risk in industry. Risk management HD61 Ahmed J. Al-Bawi Alaa M. Al-Abadi Biswajeet Pradhan Abdullah M. Alamri Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers
description	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.
format	article
author	Ahmed J. Al-Bawi Alaa M. Al-Abadi Biswajeet Pradhan Abdullah M. Alamri
author_facet	Ahmed J. Al-Bawi Alaa M. Al-Abadi Biswajeet Pradhan Abdullah M. Alamri
author_sort	Ahmed J. Al-Bawi
title	Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers
title_short	Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers
title_full	Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers
title_fullStr	Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers
title_full_unstemmed	Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers
title_sort	assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/a4f3cee1e20f41c5979b521a89d6b7af
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Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers

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