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Differentiation of multi-parametric groups of groundwater bodies through discriminant analysis and machine Learning

2023Journal article Open access
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dc.contributor.authorMohsine, Ismail
dc.contributor.authorKacimi, Ilias
dc.contributor.authorValles, Vincent
dc.contributor.authorLeblanc, Marc
dc.contributor.authorEl Mahrad, Badr
dc.contributor.authorDassonville, Fabrice
dc.contributor.authorKassou, Nadia
dc.contributor.authorBouramtane, Tarik
dc.contributor.authorAbraham, Shiny
dc.contributor.authorTouiouine, Abdessamad
dc.contributor.authorJabrane, Meryem
dc.contributor.authorTouzani, Meryem
dc.contributor.authorBarry, Abdoul Azize
dc.contributor.authorYameogo, Suzanne
dc.contributor.authorBarbiero, Laurent
dc.date.accessioned2024-01-18T10:55:28Z
dc.date.available2024-01-18T10:55:28Z
dc.date.issued2023
dc.description.abstractIn order to facilitate the monitoring of groundwater quality in France, the groundwater bodies (GWB) in the Provence-Alpes-Cote d'Azur region have been grouped into 11 homogeneous clusters on the basis of their physico-chemical and bacteriological characteristics. This study aims to test the legitimacy of this grouping by predicting whether water samples belong to a given sampling point, GWB or group of GWBs. To this end, 8673 observations and 18 parameters were extracted from the Size-Eaux database, and this dataset was processed using discriminant analysis and various machine learning algorithms. The results indicate an accuracy of 67% using linear discriminant analysis and 69 to 83% using ML algorithms, while quadratic discriminant analysis underperforms in comparison, yielding a less accurate prediction of 59%. The importance of each parameter in the prediction was assessed using an approach combining recursive feature elimination (RFE) techniques and random forest feature importance (RFFI). Major ions show high spatial range and play the main role in discrimination, while trace elements and bacteriological parameters of high local and/or temporal variability only play a minor role. The disparity of the results according to the characteristics of the GWB groups (geography, altitude, lithology, etc.) is discussed. Validating the grouping of GWBs will enable monitoring and surveillance strategies to be redirected on the basis of fewer, homogeneous hydrogeological units, in order to optimize sustainable management of the resource by the health agencies.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/hydrology10120230pt_PT
dc.identifier.eissn2306-5338
dc.identifier.urihttp://hdl.handle.net/10400.1/20311
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectGroundwater bodiespt_PT
dc.subjectMachine learningpt_PT
dc.subjectDiscriminant analysispt_PT
dc.subjectChemical compositionpt_PT
dc.subjectBacteriological compositionpt_PT
dc.subjectPACA regionpt_PT
dc.subjectFrancept_PT
dc.titleDifferentiation of multi-parametric groups of groundwater bodies through discriminant analysis and machine Learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue12pt_PT
oaire.citation.startPage230pt_PT
oaire.citation.titleHydrologypt_PT
oaire.citation.volume10pt_PT
person.familyNameEl Mahrad
person.givenNameBadr
person.identifier702391
person.identifier.ciencia-id1414-4FFE-F235
person.identifier.orcid0000-0001-6485-0539
person.identifier.scopus-author-id57209271531
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationec0db23f-77b1-4d57-8011-8930aa8d0509
relation.isAuthorOfPublication.latestForDiscoveryec0db23f-77b1-4d57-8011-8930aa8d0509

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