A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Sajedi-Hosseini, F; Malekian, A; Choubin, B; Rahmati, O; Cipullo, S; Coulon, F; Pradhan, B

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Sajedi-Hosseini, F Malekian, A Choubin, B Rahmati, O Cipullo, S Coulon, F Pradhan, B

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Publication Type:: Journal Article
Citation:: Science of the Total Environment, 2018, 644 pp. 954 - 962
Issue Date:: 2018-12-10

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Field	Value	Language
dc.contributor.author	Sajedi-Hosseini, F	en_US
dc.contributor.author	Malekian, A	en_US
dc.contributor.author	Choubin, B	en_US
dc.contributor.author	Rahmati, O	en_US
dc.contributor.author	Cipullo, S	en_US
dc.contributor.author	Coulon, F	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.available	2018-07-04	en_US
dc.date.issued	2018-12-10	en_US
dc.identifier.citation	Science of the Total Environment, 2018, 644 pp. 954 - 962	en_US
dc.identifier.issn	0048-9697	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130802
dc.description.abstract	© 2018 Elsevier B.V. This study aimed to develop a novel framework for risk assessment of nitrate groundwater contamination by integrating chemical and statistical analysis for an arid region. A standard method was applied for assessing the vulnerability of groundwater to nitrate pollution in Lenjanat plain, Iran. Nitrate concentration were collected from 102 wells of the plain and used to provide pollution occurrence and probability maps. Three machine learning models including boosted regression trees (BRT), multivariate discriminant analysis (MDA), and support vector machine (SVM) were used for the probability of groundwater pollution occurrence. Afterwards, an ensemble modeling approach was applied for production of the groundwater pollution occurrence probability map. Validation of the models was carried out using area under the receiver operating characteristic curve method (AUC); values above 80% were selected to contribute in ensembling process. Results indicated that accuracy for the three models ranged from 0.81 to 0.87, therefore all models were considered for ensemble modeling process. The resultant groundwater pollution risk (produced by vulnerability, pollution, and probability maps) indicated that the central regions of the plain have high and very high risk of nitrate pollution further confirmed by the exiting landuse map. The findings may provide very helpful information in decision making for groundwater pollution risk management especially in semi-arid regions.	en_US
dc.relation.ispartof	Science of the Total Environment	en_US
dc.relation.isbasedon	10.1016/j.scitotenv.2018.07.054	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Environmental Sciences	en_US
dc.title	A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination	en_US
dc.type	Journal Article
utslib.citation.volume	644	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	644	en_US

Abstract:

© 2018 Elsevier B.V. This study aimed to develop a novel framework for risk assessment of nitrate groundwater contamination by integrating chemical and statistical analysis for an arid region. A standard method was applied for assessing the vulnerability of groundwater to nitrate pollution in Lenjanat plain, Iran. Nitrate concentration were collected from 102 wells of the plain and used to provide pollution occurrence and probability maps. Three machine learning models including boosted regression trees (BRT), multivariate discriminant analysis (MDA), and support vector machine (SVM) were used for the probability of groundwater pollution occurrence. Afterwards, an ensemble modeling approach was applied for production of the groundwater pollution occurrence probability map. Validation of the models was carried out using area under the receiver operating characteristic curve method (AUC); values above 80% were selected to contribute in ensembling process. Results indicated that accuracy for the three models ranged from 0.81 to 0.87, therefore all models were considered for ensemble modeling process. The resultant groundwater pollution risk (produced by vulnerability, pollution, and probability maps) indicated that the central regions of the plain have high and very high risk of nitrate pollution further confirmed by the exiting landuse map. The findings may provide very helpful information in decision making for groundwater pollution risk management especially in semi-arid regions.

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http://hdl.handle.net/10453/130802