Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin

This paper presents a machine learning method to map groundwater potential in crystalline domains. First, a spatially-distributed set of explanatory variables for groundwater occurrence is compiled into a geographic information system. Twenty machine learning classifiers are subsequently trained on...

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Detalhes bibliográficos
Autores: Gómez-Escalonilla Canales, Víctor, Vogt, Marie-Louise, Destro, Elisa, Isseini, Moussa, Origgi, Giaime, Djoret, Daira, Martínez Santos, Pedro, Holecz, Francesco
Tipo de documento: artigo
Data de publicação:2021
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositório:Docta Complutense
Idioma:inglês
OAI Identifier:oai:docta.ucm.es:20.500.14352/6765
Acesso em linha:https://hdl.handle.net/20.500.14352/6765
Access Level:Acceso aberto
Palavra-chave:556.3(674.3)
Remote sensing
groundwater exploration
machine learning
Lake Chad basin
Hidrología
2508 Hidrología
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oai_identifier_str oai:docta.ucm.es:20.500.14352/6765
network_acronym_str ES
network_name_str España
repository_id_str
spelling Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basinGómez-Escalonilla Canales, VíctorVogt, Marie-LouiseDestro, ElisaIsseini, MoussaOriggi, GiaimeDjoret, DairaMartínez Santos, PedroHolecz, Francesco556.3(674.3)Remote sensinggroundwater explorationmachine learningLake Chad basinHidrología2508 HidrologíaThis paper presents a machine learning method to map groundwater potential in crystalline domains. First, a spatially-distributed set of explanatory variables for groundwater occurrence is compiled into a geographic information system. Twenty machine learning classifiers are subsequently trained on a sample of 488 boreholes and excavated wells for a region of eastern Chad. This process includes collinearity, cross-validation, feature elimination and parameter fitting routines. Random forest and extra trees classifiers outperformed other algorithms (test score > 0.80, balanced score > 0.80, AUC > 0.87). Fracture density, slope, SAR coherence (interferometric correlation), topographic wetness index, basement depth, distance to channels and slope aspect proved the most relevant explanatory variables. Three major conclusions stem from this work: (1) using a large number of supervised classification algorithms is advisable in groundwater potential studies; (2) the choice of performance metrics constrains the relevance of explanatory variables; and (3) seasonal variations from satellite images contribute to successful groundwater potential mapping.Taylor and FrancisUniversidad Complutense de Madrid20212021-01-0120212021-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/6765reponame: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:20.500.14352/67652026-06-02T12:44:21Z
dc.title.none.fl_str_mv Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
title Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
spellingShingle Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
Gómez-Escalonilla Canales, Víctor
556.3(674.3)
Remote sensing
groundwater exploration
machine learning
Lake Chad basin
Hidrología
2508 Hidrología
title_short Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
title_full Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
title_fullStr Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
title_full_unstemmed Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
title_sort Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
dc.creator.none.fl_str_mv Gómez-Escalonilla Canales, Víctor
Vogt, Marie-Louise
Destro, Elisa
Isseini, Moussa
Origgi, Giaime
Djoret, Daira
Martínez Santos, Pedro
Holecz, Francesco
author Gómez-Escalonilla Canales, Víctor
author_facet Gómez-Escalonilla Canales, Víctor
Vogt, Marie-Louise
Destro, Elisa
Isseini, Moussa
Origgi, Giaime
Djoret, Daira
Martínez Santos, Pedro
Holecz, Francesco
author_role author
author2 Vogt, Marie-Louise
Destro, Elisa
Isseini, Moussa
Origgi, Giaime
Djoret, Daira
Martínez Santos, Pedro
Holecz, Francesco
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 556.3(674.3)
Remote sensing
groundwater exploration
machine learning
Lake Chad basin
Hidrología
2508 Hidrología
topic 556.3(674.3)
Remote sensing
groundwater exploration
machine learning
Lake Chad basin
Hidrología
2508 Hidrología
description This paper presents a machine learning method to map groundwater potential in crystalline domains. First, a spatially-distributed set of explanatory variables for groundwater occurrence is compiled into a geographic information system. Twenty machine learning classifiers are subsequently trained on a sample of 488 boreholes and excavated wells for a region of eastern Chad. This process includes collinearity, cross-validation, feature elimination and parameter fitting routines. Random forest and extra trees classifiers outperformed other algorithms (test score > 0.80, balanced score > 0.80, AUC > 0.87). Fracture density, slope, SAR coherence (interferometric correlation), topographic wetness index, basement depth, distance to channels and slope aspect proved the most relevant explanatory variables. Three major conclusions stem from this work: (1) using a large number of supervised classification algorithms is advisable in groundwater potential studies; (2) the choice of performance metrics constrains the relevance of explanatory variables; and (3) seasonal variations from satellite images contribute to successful groundwater potential mapping.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01
2021
2021-01-01
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/20.500.14352/6765
url https://hdl.handle.net/20.500.14352/6765
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 Taylor and Francis
publisher.none.fl_str_mv Taylor and Francis
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
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score 15.228081

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