Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS

Golkarian, A; Naghibi, SA; Kalantar, B; Pradhan, B

Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS

Golkarian, A Naghibi, SA Kalantar, B Pradhan, B

Permalink

Publication Type:: Journal Article
Citation:: Environmental Monitoring and Assessment, 2018, 190 (3)
Issue Date:: 2018-03-01

Closed Access

	Filename	Description	Size
	Golkarian2018_Article_GroundwaterPotentialMappingUsi.pdf	Published Version	2.37 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Golkarian, A	en_US
dc.contributor.author	Naghibi, SA	en_US
dc.contributor.author	Kalantar, B	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.available	2018-01-24	en_US
dc.date.issued	2018-03-01	en_US
dc.identifier.citation	Environmental Monitoring and Assessment, 2018, 190 (3)	en_US
dc.identifier.issn	0167-6369	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130831
dc.description.abstract	© 2018, Springer International Publishing AG, part of Springer Nature. Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.	en_US
dc.relation.ispartof	Environmental Monitoring and Assessment	en_US
dc.relation.isbasedon	10.1007/s10661-018-6507-8	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Sciences	en_US
dc.subject.mesh	Multivariate Analysis	en_US
dc.subject.mesh	Regression Analysis	en_US
dc.subject.mesh	ROC Curve	en_US
dc.subject.mesh	Desert Climate	en_US
dc.subject.mesh	Rivers	en_US
dc.subject.mesh	Environmental Monitoring	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Theoretical	en_US
dc.subject.mesh	Geographic Information Systems	en_US
dc.subject.mesh	Iran	en_US
dc.subject.mesh	Groundwater	en_US
dc.subject.mesh	Water Resources	en_US
dc.title	Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	190	en_US
utslib.for	0907 Environmental Engineering	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	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	closed_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	190	en_US

Abstract:

© 2018, Springer International Publishing AG, part of Springer Nature. Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.

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

http://hdl.handle.net/10453/130831