A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

Bui, DT; Shirzadi, A; Shahabi, H; Chapi, K; Omidavr, E; Pham, BT; Asl, DT; Khaledian, H; Pradhan, B; Panahi, M; Bin Ahmad, B; Rahmani, H; Gróf, G; Lee, S

A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

Bui, DT Shirzadi, A

Shahabi, H

Chapi, K

Omidavr, E Pham, BT

Asl, DT Khaledian, H Pradhan, B

Panahi, M

Bin Ahmad, B Rahmani, H

Gróf, G

Lee, S

Permalink

Publication Type:: Journal Article
Citation:: Sensors (Switzerland), 2019, 19 (11)
Issue Date:: 2019-06-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published VersionAdobe PDF (6.84 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Bui, DT	en_US
dc.contributor.author	Shirzadi, A https://orcid.org/0000-0001-9668-8687	en_US
dc.contributor.author	Shahabi, H https://orcid.org/0000-0001-5091-6947	en_US
dc.contributor.author	Chapi, K https://orcid.org/0000-0002-9466-665X	en_US
dc.contributor.author	Omidavr, E	en_US
dc.contributor.author	Pham, BT https://orcid.org/0000-0001-9707-840X	en_US
dc.contributor.author	Asl, DT	en_US
dc.contributor.author	Khaledian, H	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Panahi, M https://orcid.org/0000-0001-7601-9208	en_US
dc.contributor.author	Bin Ahmad, B	en_US
dc.contributor.author	Rahmani, H https://orcid.org/0000-0003-3418-4912	en_US
dc.contributor.author	Gróf, G https://orcid.org/0000-0002-6359-768X	en_US
dc.contributor.author	Lee, S https://orcid.org/0000-0003-0409-8263	en_US
dc.date.available	2019-05-18	en_US
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	Sensors (Switzerland), 2019, 19 (11)	en_US
dc.identifier.issn	1424-8220	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134796
dc.description.abstract	© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).	en_US
dc.relation.ispartof	Sensors (Switzerland)	en_US
dc.relation.isbasedon	10.3390/s19112444	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Analytical Chemistry	en_US
dc.title	A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	19	en_US
utslib.for	0301 Analytical Chemistry	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0502 Environmental Science and Management	en_US
utslib.for	0602 Ecology	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	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/134796