Perdition of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Arabameri, A; Chandra Pal, S; Costache, R; Saha, A; Rezaie, F; Seyed Danesh, A; Pradhan, B; Lee, S; Hoang, N-D

Perdition of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Arabameri, A Chandra Pal, S Costache, R Saha, A Rezaie, F Seyed Danesh, A Pradhan, B

Lee, S Hoang, N-D

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Publisher:: Taylor & Francis Open Access
Publication Type:: Journal Article
Citation:: Geomatics, Natural Hazards and Risk, 2021, 12, (1), pp. 469-498
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Arabameri, A
dc.contributor.author	Chandra Pal, S
dc.contributor.author	Costache, R
dc.contributor.author	Saha, A
dc.contributor.author	Rezaie, F
dc.contributor.author	Seyed Danesh, A
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Lee, S
dc.contributor.author	Hoang, N-D
dc.date.accessioned	2022-02-21T05:19:04Z
dc.date.available	2022-02-21T05:19:04Z
dc.date.issued	2021-01-01
dc.identifier.citation	Geomatics, Natural Hazards and Risk, 2021, 12, (1), pp. 469-498
dc.identifier.issn	1947-5705
dc.identifier.issn	1947-5713
dc.identifier.uri	http://hdl.handle.net/10453/154732
dc.description.abstract	Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and to change land-use planning. This work is exploring and researching the potential of a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution for spatial mapping of the susceptibility of gully erosion. The new machine learning approach is to combine the extreme gradient boosting machine (XGBoost) and the genetic algorithm (GA). The GA metaheuristic is being used to improve the efficiency of the XGBoost classification approach. A GIS database has been developed that contains recorded instances of gully erosion incidents and 18 conditioning variables. These parameters are used as predictive variables used to assess the condition of non-erosion or erosion in a given region within the Kohpayeh-Sagzi River Watershed research area in Iran. Exploratory results indicate that the proposed GE-XGBoost model is superior to the other benchmark solution with the desired predictive precision (89.56%). Therefore, the newly built model may be a promising method for large-scale mapping of gully erosion susceptibility.
dc.language	English
dc.publisher	Taylor & Francis Open Access
dc.relation.ispartof	Geomatics, Natural Hazards and Risk
dc.relation.isbasedon	10.1080/19475705.2021.1880977
dc.rights	This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0406 Physical Geography and Environmental Geoscience
dc.title	Perdition of gully erosion susceptibility mapping using novel ensemble machine learning algorithms
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	recently_added	*
pubs.consider-herdc	false
dc.date.updated	2022-02-21T05:19:02Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	1

Abstract:

Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and to change land-use planning. This work is exploring and researching the potential of a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution for spatial mapping of the susceptibility of gully erosion. The new machine learning approach is to combine the extreme gradient boosting machine (XGBoost) and the genetic algorithm (GA). The GA metaheuristic is being used to improve the efficiency of the XGBoost classification approach. A GIS database has been developed that contains recorded instances of gully erosion incidents and 18 conditioning variables. These parameters are used as predictive variables used to assess the condition of non-erosion or erosion in a given region within the Kohpayeh-Sagzi River Watershed research area in Iran. Exploratory results indicate that the proposed GE-XGBoost model is superior to the other benchmark solution with the desired predictive precision (89.56%). Therefore, the newly built model may be a promising method for large-scale mapping of gully erosion susceptibility.

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http://hdl.handle.net/10453/154732