Spatial modelling of Gully erosion using GIS and R programing: A comparison among three data mining algorithms

Arabameri, A; Pradhan, B; Pourghasemi, HR; Rezaei, K; Kerle, N

Spatial modelling of Gully erosion using GIS and R programing: A comparison among three data mining algorithms

Arabameri, A Pradhan, B

Pourghasemi, HR Rezaei, K Kerle, N

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Publication Type:: Journal Article
Citation:: Applied Sciences (Switzerland), 2018, 8 (8)
Issue Date:: 2018-08-14

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Field	Value	Language
dc.contributor.author	Arabameri, A	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Pourghasemi, HR	en_US
dc.contributor.author	Rezaei, K	en_US
dc.contributor.author	Kerle, N	en_US
dc.date.issued	2018-08-14	en_US
dc.identifier.citation	Applied Sciences (Switzerland), 2018, 8 (8)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130811
dc.description.abstract	© 2018 by the authors. Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods-Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)-for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locationsweremapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.	en_US
dc.relation.ispartof	Applied Sciences (Switzerland)	en_US
dc.relation.isbasedon	10.3390/app8081369	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Spatial modelling of Gully erosion using GIS and R programing: A comparison among three data mining algorithms	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
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 Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

© 2018 by the authors. Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods-Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)-for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locationsweremapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

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http://hdl.handle.net/10453/130811