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

Study 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...

ver descrição completa

Detalhes bibliográficos
Autores: Gómez Escalonilla, Víctor, Diancoumba, Oumou, Traoré, D.Y., Montero González, Esperanza, Martín Loeches, Miguel Martín, Martínez Santos, Pedro
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/72575
Acesso em linha:https://hdl.handle.net/20.500.14352/72575
Access Level:acceso abierto
Palavra-chave:556.3
Machine learning
Groundwater exploration
Yield prediction
GIS
Mali
Hidrología
2508 Hidrología
id ES_276b1ea0f6b3ffb013ca18ffd116f199
oai_identifier_str oai:docta.ucm.es:20.500.14352/72575
network_acronym_str ES
network_name_str España
repository_id_str
spelling Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiersGómez Escalonilla, VíctorDiancoumba, OumouTraoré, D.Y.Montero González, EsperanzaMartín Loeches, Miguel MartínMartínez Santos, Pedro556.3Machine learningGroundwater explorationYield predictionGISMaliHidrología2508 HidrologíaStudy 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.ElsevierUniversidad Complutense de Madrid20222022-01-0120222022-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/72575reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttps://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/725752026-06-02T12:44:21Z
dc.title.none.fl_str_mv Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
title Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
spellingShingle Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
Gómez Escalonilla, Víctor
556.3
Machine learning
Groundwater exploration
Yield prediction
GIS
Mali
Hidrología
2508 Hidrología
title_short Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
title_full Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
title_fullStr Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
title_full_unstemmed Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
title_sort Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
dc.creator.none.fl_str_mv Gómez Escalonilla, Víctor
Diancoumba, Oumou
Traoré, D.Y.
Montero González, Esperanza
Martín Loeches, Miguel Martín
Martínez Santos, Pedro
author Gómez Escalonilla, Víctor
author_facet Gómez Escalonilla, Víctor
Diancoumba, Oumou
Traoré, D.Y.
Montero González, Esperanza
Martín Loeches, Miguel Martín
Martínez Santos, Pedro
author_role author
author2 Diancoumba, Oumou
Traoré, D.Y.
Montero González, Esperanza
Martín Loeches, Miguel Martín
Martínez Santos, Pedro
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 556.3
Machine learning
Groundwater exploration
Yield prediction
GIS
Mali
Hidrología
2508 Hidrología
topic 556.3
Machine learning
Groundwater exploration
Yield prediction
GIS
Mali
Hidrología
2508 Hidrología
description Study 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01
2022
2022-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/72575
url https://hdl.handle.net/20.500.14352/72575
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
https://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
https://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404885499248640
score 15.300719

Registos relacionados