Groundwater potential mapping using a novel data-mining ensemble model

Kordestani, MD; Naghibi, SA; Hashemi, H; Ahmadi, K; Kalantar, B; Pradhan, B

Groundwater potential mapping using a novel data-mining ensemble model

Kordestani, MD Naghibi, SA Hashemi, H Ahmadi, K Kalantar, B

Pradhan, B

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Publication Type:: Journal Article
Citation:: Hydrogeology Journal, 2019, 27 (1), pp. 211 - 224
Issue Date:: 2019-02-13

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Field	Value	Language
dc.contributor.author	Kordestani, MD	en_US
dc.contributor.author	Naghibi, SA	en_US
dc.contributor.author	Hashemi, H	en_US
dc.contributor.author	Ahmadi, K	en_US
dc.contributor.author	Kalantar, B https://orcid.org/0000-0002-2822-3463	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.available	2020-05-25T19:17:05Z
dc.date.issued	2019-02-13	en_US
dc.identifier.citation	Hydrogeology Journal, 2019, 27 (1), pp. 211 - 224	en_US
dc.identifier.issn	1431-2174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130841
dc.description.abstract	© 2018, The Author(s). Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model’s output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.	en_US
dc.relation.ispartof	Hydrogeology Journal	en_US
dc.relation.isbasedon	10.1007/s10040-018-1848-5	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Environmental Engineering	en_US
dc.title	Groundwater potential mapping using a novel data-mining ensemble model	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	27	en_US
utslib.for	0401 Atmospheric Sciences	en_US
utslib.for	0904 Chemical Engineering	en_US
utslib.for	04 Earth Sciences	en_US
utslib.for	09 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	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

© 2018, The Author(s). Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model’s output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.

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http://hdl.handle.net/10453/130841