Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers

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dc.contributor.authorGómez Escalonilla, Víctor
dc.contributor.authorDiancoumba, Oumou
dc.contributor.authorTraoré, D.Y.
dc.contributor.authorMontero González, Esperanza
dc.contributor.authorMartín Loeches, Miguel Martín
dc.contributor.authorMartínez Santos, Pedro
dc.date.accessioned2023-06-22T12:27:57Z
dc.date.available2023-06-22T12:27:57Z
dc.date.issued2022
dc.descriptionCRUE-CSIC (Acuerdos Transformativos 2022)
dc.description.abstractStudy region: Regions of Bamako, Kati and Kangaba, southwestern Mali Study focus: Machine learning-based mapping of borehole yield. Three algorithms were trained on an imbalanced multiclass database of boreholes, while twenty variables were used as predictors for borehole yield. All models returned balanced and geometric scores in the order of 0.80, with area under the receiver operating characteristic curve up to 0.87. Three main methodological conclusions are drawn: (a) The evaluation of different machine learning classifiers and various resampling strategies and the subsequent selection of the best performing ones is shown to be a good strategy in this type of studies; (b) ad hoc calibration tools, such as data on borehole success rates, provide an apt complement to standard machine learning metrics; and (c) a multiclass approach with an unbalanced database represents a greater challenge than predicting a bivariate outcome, but potentially results in a finer depiction of field conditions. New hydrological insights for the region: Alluvial sediments were found to be the most productive areas, while the Mandingue Plateau has the lowest groundwater potential. The piedmont areas showcase an intermediate groundwater prospect. Elevation, basement depth, slope and geology rank among the most important variables. Lower values of clay content, slopes and elevations, and higher values of basement depth and saturated thickness were linked to the most productive class.
dc.description.departmentDepto. de Geodinámica, Estratigrafía y Paleontología
dc.description.facultyFac. de Ciencias Geológicas
dc.description.refereedTRUE
dc.description.sponsorshipUnión Europea. Horizonte 2020
dc.description.sponsorshipMinisterio de Ciencia e Innovación (MICINN)
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/75366
dc.identifier.doi10.1016/j.ejrh.2022.101245
dc.identifier.issn22145818
dc.identifier.officialurlhttps://doi.org/10.1016/j.ejrh.2022.101245
dc.identifier.urihttps://hdl.handle.net/20.500.14352/72575
dc.issue.number101245
dc.journal.titleJournal of Hydrology. Regional studies
dc.language.isoeng
dc.page.final21
dc.page.initial1
dc.publisherElsevier
dc.relation.projectIDSTARS4Water (101059372)
dc.relation.projectIDPID2021-124018OB-I00; RTI2018-099394-B-I00; PRE2019–090026
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.cdu556.3
dc.subject.keywordMachine learning
dc.subject.keywordGroundwater exploration
dc.subject.keywordYield prediction
dc.subject.keywordGIS
dc.subject.keywordMali
dc.subject.ucmHidrología
dc.subject.unesco2508 Hidrología
dc.titleMulticlass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
dc.typejournal article
dc.volume.number44
dspace.entity.typePublication
relation.isAuthorOfPublicatione2928ae4-1df3-472d-a525-27c92d5ffddc
relation.isAuthorOfPublicationfe2f5bb2-2318-4316-b695-cfeff52d3e6e
relation.isAuthorOfPublication.latestForDiscoverye2928ae4-1df3-472d-a525-27c92d5ffddc
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