Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco

Detalhes bibliográficos
Autor(a) principal: Jadoud, Mourad
Data de Publicação: 2023
Outros Autores: El Achheb, Abderrahim, Laftouhi, Noureddine, Namous, Mustapha, Khouz, Abdellah, Trindade, Jorge, El Bchari, Fatima, Bougadir, Blaid, Eloudi, Hasna, Rachidi, Said
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.2/14374
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spelling Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, MoroccoRepositório AbertoJadoud, MouradEl Achheb, AbderrahimLaftouhi, NoureddineNamous, MustaphaKhouz, AbdellahTrindade, JorgeEl Bchari, FatimaBougadir, BlaidEloudi, HasnaRachidi, Said2023-07-12T18:00:57Z20232023-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10400.2/14374engJADOUD, M., EL ACHHEB, A., LAFTOUHI, N., NAMOUS, M., KHOUZ, A., TRINDADE, J., EL BCHARI, F., BOUGADIR, B., ELOUDI, H., RACHIDI, S. (2023) Machine Learning Models for Spatial Prediction of Groundwater Potentiality in a Large Semi-Arid Mountainous Region: Application to the Rherhaya Watershed, High Atlas, Morocco. EGU General Assembly 2023, Geophysical Research Abstracts, EGU23- 16375. 23 a 28 abril, Viena, Áustria.https://doi.org/10.5194/egusphere-egu23-16375info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-11-24T01:46:29Zoai:repositorioaberto.uab.pt:10400.2/14374Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-11-24T01:46:29Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco
title Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco
spellingShingle Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco
Jadoud, Mourad
title_short Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco
title_full Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco
title_fullStr Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco
title_full_unstemmed Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco
title_sort Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco
author Jadoud, Mourad
author_facet Jadoud, Mourad
El Achheb, Abderrahim
Laftouhi, Noureddine
Namous, Mustapha
Khouz, Abdellah
Trindade, Jorge
El Bchari, Fatima
Bougadir, Blaid
Eloudi, Hasna
Rachidi, Said
author_role author
author2 El Achheb, Abderrahim
Laftouhi, Noureddine
Namous, Mustapha
Khouz, Abdellah
Trindade, Jorge
El Bchari, Fatima
Bougadir, Blaid
Eloudi, Hasna
Rachidi, Said
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Aberto
dc.contributor.author.fl_str_mv Jadoud, Mourad
El Achheb, Abderrahim
Laftouhi, Noureddine
Namous, Mustapha
Khouz, Abdellah
Trindade, Jorge
El Bchari, Fatima
Bougadir, Blaid
Eloudi, Hasna
Rachidi, Said
publishDate 2023
dc.date.none.fl_str_mv 2023-07-12T18:00:57Z
2023
2023-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.2/14374
url http://hdl.handle.net/10400.2/14374
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv JADOUD, M., EL ACHHEB, A., LAFTOUHI, N., NAMOUS, M., KHOUZ, A., TRINDADE, J., EL BCHARI, F., BOUGADIR, B., ELOUDI, H., RACHIDI, S. (2023) Machine Learning Models for Spatial Prediction of Groundwater Potentiality in a Large Semi-Arid Mountainous Region: Application to the Rherhaya Watershed, High Atlas, Morocco. EGU General Assembly 2023, Geophysical Research Abstracts, EGU23- 16375. 23 a 28 abril, Viena, Áustria.
https://doi.org/10.5194/egusphere-egu23-16375
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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