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

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dc.contributor.authorSajedi-Hosseini, Farzaneh
dc.contributor.authorMalekian, Arash
dc.contributor.authorChoubin, Bahram
dc.contributor.authorRahmati, Omid
dc.contributor.authorCipullo, Sabrina
dc.contributor.authorCoulon, Frederic
dc.contributor.authorPradhan, Biswajeet
dc.date.accessioned2018-07-13T08:08:51Z
dc.date.available2018-07-13T08:08:51Z
dc.date.issued2018-07-11
dc.description.abstractThis 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_UK
dc.identifier.citationSajedi-Hosseini F, Malekian A, Choubin B, et al., (2018) A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. Science of The Total Environment, Volume 644, December 2018, pp. 954-962en_UK
dc.identifier.issn0048-9697
dc.identifier.urihttps://doi.org/10.1016/j.scitotenv.2018.07.054
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/13340
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectGroundwater pollutionen_UK
dc.subjectNitrateen_UK
dc.subjectProbabilityen_UK
dc.subjectRisken_UK
dc.subjectVulnerabilityen_UK
dc.subjectGISen_UK
dc.titleA novel machine learning-based approach for the risk assessment of nitrate groundwater contaminationen_UK
dc.typeArticleen_UK

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