Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion

Aboutaib, F; Krimissa, S; Pradhan, B; Elaloui, A; Ismaili, M; Abdelrahman, K; Eloudi, H; Ouayah, M; Ourribane, M; Namous, M

Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion

Aboutaib, F Krimissa, S Pradhan, B

Elaloui, A Ismaili, M Abdelrahman, K Eloudi, H Ouayah, M Ourribane, M Namous, M

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Publisher:: Frontiers
Publication Type:: Journal Article
Citation:: Frontiers in Environmental Science, 2023, 11, pp. 1207027
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Aboutaib, F
dc.contributor.author	Krimissa, S
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Elaloui, A
dc.contributor.author	Ismaili, M
dc.contributor.author	Abdelrahman, K
dc.contributor.author	Eloudi, H
dc.contributor.author	Ouayah, M
dc.contributor.author	Ourribane, M
dc.contributor.author	Namous, M
dc.date.accessioned	2023-11-08T00:04:18Z
dc.date.available	2023-11-08T00:04:18Z
dc.date.issued	2023-01-01
dc.identifier.citation	Frontiers in Environmental Science, 2023, 11, pp. 1207027
dc.identifier.issn	2296-665X
dc.identifier.issn	2296-665X
dc.identifier.uri	http://hdl.handle.net/10453/173215
dc.description.abstract	Assessing and mapping the vulnerability of gully erosion in mountainous and semi-arid areas is a crucial field of research due to the significant environmental degradation observed in such regions. In order to tackle this problem, the present study aims to evaluate the effectiveness of three commonly used machine learning models: Random Forest, Support Vector Machine, and Logistic Regression. Several geographic and environmental factors including topographic, geomorphological, environmental, and hydrologic factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 191 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. The models’ performance was assessed by calculating the area under the ROC curve (AUC). The findings indicate that the RF model exhibited the highest performance (AUC = 89%), followed by the SVM (AUC = 87%) and LR (AUC = 87%) models. Furthermore, the results highlight those factors such as NDVI, lithology, drainage, and density were the most influential, as determined by the RF, SVM, and LR methods. This study provides a valuable tool for enhancing the mapping of soil erosion and identifying the most important influencing factors that primarily cause soil deterioration in mountainous and semi-arid regions.
dc.language	en
dc.publisher	Frontiers
dc.relation.ispartof	Frontiers in Environmental Science
dc.relation.isbasedon	10.3389/fenvs.2023.1207027
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0502 Environmental Science and Management
dc.subject.classification	4104 Environmental management
dc.subject.classification	4105 Pollution and contamination
dc.title	Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0502 Environmental Science and Management
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	*
dc.date.updated	2023-11-08T00:04:17Z
pubs.publication-status	Published
pubs.volume	11

Abstract:

Assessing and mapping the vulnerability of gully erosion in mountainous and semi-arid areas is a crucial field of research due to the significant environmental degradation observed in such regions. In order to tackle this problem, the present study aims to evaluate the effectiveness of three commonly used machine learning models: Random Forest, Support Vector Machine, and Logistic Regression. Several geographic and environmental factors including topographic, geomorphological, environmental, and hydrologic factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 191 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. The models’ performance was assessed by calculating the area under the ROC curve (AUC). The findings indicate that the RF model exhibited the highest performance (AUC = 89%), followed by the SVM (AUC = 87%) and LR (AUC = 87%) models. Furthermore, the results highlight those factors such as NDVI, lithology, drainage, and density were the most influential, as determined by the RF, SVM, and LR methods. This study provides a valuable tool for enhancing the mapping of soil erosion and identifying the most important influencing factors that primarily cause soil deterioration in mountainous and semi-arid regions.

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http://hdl.handle.net/10453/173215