Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis

Chen, W; Pradhan, B; Li, S; Shahabi, H; Rizeei, HM; Hou, E; Wang, S

Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis

Chen, W Pradhan, B

Li, S Shahabi, H Rizeei, HM

Hou, E Wang, S

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Publication Type:: Journal Article
Citation:: Natural Resources Research, 2019, 28 (4), pp. 1239 - 1258
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Chen, W	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Li, S	en_US
dc.contributor.author	Shahabi, H	en_US
dc.contributor.author	Rizeei, HM https://orcid.org/0000-0002-7690-8440	en_US
dc.contributor.author	Hou, E	en_US
dc.contributor.author	Wang, S	en_US
dc.date.available	2020-05-25T19:04:12Z
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	Natural Resources Research, 2019, 28 (4), pp. 1239 - 1258	en_US
dc.identifier.issn	1520-7439	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130785
dc.description.abstract	© 2019, International Association for Mathematical Geosciences. Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of Fisher’s linear discriminant function (FLDA) with rotation forest (RFLDA) and bagging (BFLDA) ensembles was used for groundwater potential assessment at the Ningtiaota area in Shaanxi, China. A spatial database with 66 groundwater spring locations and 14 groundwater spring contributing factors was prepared; these factors were elevation, aspect, slope, plan and profile curvatures, sediment transport index, stream power index, topographic wetness index, distance to roads and streams, land use, lithology, soil and normalized difference vegetation index. The classifier attribute evaluation method based on the FLDA model was implemented to test the predictive competence of the mentioned contributing factors. The area under curve, confidence interval at 95%, standard error, Friedman test and Wilcoxon signed-rank test were used to compare and validate the success and prediction competence of the three applied models. According to the achieved results, the BFLDA model showed the most prediction competence, followed by the RFLDA and FLDA models, respectively. The resulting groundwater spring potential maps can be used for groundwater development plans and land use planning.	en_US
dc.relation.ispartof	Natural Resources Research	en_US
dc.relation.isbasedon	10.1007/s11053-019-09465-w	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Geochemistry & Geophysics	en_US
dc.title	Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	28	en_US
utslib.for	0502 Environmental Science and Management	en_US
utslib.for	0914 Resources Engineering and Extractive Metallurgy	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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

© 2019, International Association for Mathematical Geosciences. Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of Fisher’s linear discriminant function (FLDA) with rotation forest (RFLDA) and bagging (BFLDA) ensembles was used for groundwater potential assessment at the Ningtiaota area in Shaanxi, China. A spatial database with 66 groundwater spring locations and 14 groundwater spring contributing factors was prepared; these factors were elevation, aspect, slope, plan and profile curvatures, sediment transport index, stream power index, topographic wetness index, distance to roads and streams, land use, lithology, soil and normalized difference vegetation index. The classifier attribute evaluation method based on the FLDA model was implemented to test the predictive competence of the mentioned contributing factors. The area under curve, confidence interval at 95%, standard error, Friedman test and Wilcoxon signed-rank test were used to compare and validate the success and prediction competence of the three applied models. According to the achieved results, the BFLDA model showed the most prediction competence, followed by the RFLDA and FLDA models, respectively. The resulting groundwater spring potential maps can be used for groundwater development plans and land use planning.

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http://hdl.handle.net/10453/130785