Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches

Rahmati, O; Naghibi, SA; Shahabi, H; Bui, DT; Pradhan, B; Azareh, A; Rafiei-Sardooi, E; Samani, AN; Melesse, AM

Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches

Rahmati, O Naghibi, SA Shahabi, H Bui, DT Pradhan, B

Azareh, A Rafiei-Sardooi, E Samani, AN Melesse, AM

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Publication Type:: Journal Article
Citation:: Journal of Hydrology, 2018, 565 pp. 248 - 261
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Rahmati, O	en_US
dc.contributor.author	Naghibi, SA	en_US
dc.contributor.author	Shahabi, H	en_US
dc.contributor.author	Bui, DT	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Azareh, A	en_US
dc.contributor.author	Rafiei-Sardooi, E	en_US
dc.contributor.author	Samani, AN	en_US
dc.contributor.author	Melesse, AM	en_US
dc.date.issued	2018-10-01	en_US
dc.identifier.citation	Journal of Hydrology, 2018, 565 pp. 248 - 261	en_US
dc.identifier.issn	0022-1694	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130809
dc.description.abstract	© 2018 Elsevier B.V. Sustainable water resources management in arid and semi-arid areas needs robust models, which allow accurate and reliable predictive modeling. This issue has motivated the researchers to develop hybrid models that offer solutions on modelling problems and accurate predictions of groundwater potential zonation. For this purpose, this research aims to investigate the capability and robustness of a novel hybrid model, namely the logistic model tree (LMT) and compares it with state-of-the-art models such as the support vector machine and C4.5 models that locate potential zones for groundwater springs. A spring location dataset consisting of 359 springs was provided by field surveys and national reports and from which three different sample data sets (S1–S3) were randomly prepared (70% for training and 30% for validation). Additionally, 16 spring-related factors were analyzed using regression logistic analysis to find which factors play a significant role in spring occurrence. Twelve significant geo-environmental and morphometric factors were identified and applied in all models. The accuracy of models was evaluated by three different threshold-dependent and –Independent methods including efficiency (E), true skill statistic (TSS), and area under the receiver operating characteristics curve (AUC-ROC) methods. Results showed that the LMT model had the highest accuracy performance for all three validation datasets (Emean = 0.860, TSSmean = 0.718, AUC-ROCmean = 0.904); although a slight sensitivity to change in input data was sometimes observed for this model. Furthermore, the findings showed that relative slope position (RSP) was the most important factor followed by distance from faults and lithology.	en_US
dc.relation.ispartof	Journal of Hydrology	en_US
dc.relation.isbasedon	10.1016/j.jhydrol.2018.08.027	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Engineering	en_US
dc.title	Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches	en_US
dc.type	Journal Article
utslib.citation.volume	565	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0102 Applied Mathematics	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.publication-status	Published	en_US
pubs.volume	565	en_US

Abstract:

© 2018 Elsevier B.V. Sustainable water resources management in arid and semi-arid areas needs robust models, which allow accurate and reliable predictive modeling. This issue has motivated the researchers to develop hybrid models that offer solutions on modelling problems and accurate predictions of groundwater potential zonation. For this purpose, this research aims to investigate the capability and robustness of a novel hybrid model, namely the logistic model tree (LMT) and compares it with state-of-the-art models such as the support vector machine and C4.5 models that locate potential zones for groundwater springs. A spring location dataset consisting of 359 springs was provided by field surveys and national reports and from which three different sample data sets (S1–S3) were randomly prepared (70% for training and 30% for validation). Additionally, 16 spring-related factors were analyzed using regression logistic analysis to find which factors play a significant role in spring occurrence. Twelve significant geo-environmental and morphometric factors were identified and applied in all models. The accuracy of models was evaluated by three different threshold-dependent and –Independent methods including efficiency (E), true skill statistic (TSS), and area under the receiver operating characteristics curve (AUC-ROC) methods. Results showed that the LMT model had the highest accuracy performance for all three validation datasets (Emean = 0.860, TSSmean = 0.718, AUC-ROCmean = 0.904); although a slight sensitivity to change in input data was sometimes observed for this model. Furthermore, the findings showed that relative slope position (RSP) was the most important factor followed by distance from faults and lithology.

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http://hdl.handle.net/10453/130809