Streamflow estimation in ungauged catchments in Brazil using machine learning approaches

Detalhes bibliográficos
Autor(a) principal: Fontana, Rafael Barbedo
Data de Publicação: 2023
Outros Autores: Sorribas, Mino Viana, Collischonn, Walter
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/259933
id UFRGS-2_dbecaa553b638748268634408d3c4971
oai_identifier_str oai:www.lume.ufrgs.br:10183/259933
network_acronym_str UFRGS-2
network_name_str Repositório Institucional da UFRGS
repository_id_str
spelling Fontana, Rafael BarbedoSorribas, Mino VianaCollischonn, WalterEuropean Geoscience Union. General Assembly. (2023 : Vienna : On-line)2023-07-04T03:51:07Z2023http://hdl.handle.net/10183/259933001169183application/pdfengEuropean Geoscience Union. General Assembly (2023 : Vienna : On-line). [Programme]. [Göttingen : Copernicus, 2023]Escoamento : SimulacaoBacias hidrográficasDados escassosAprendizado de máquinaStreamflow estimation in ungauged catchments in Brazil using machine learning approachesEstrangeiroinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001169183.pdf.txt001169183.pdf.txtExtracted Texttext/plain3704http://www.lume.ufrgs.br/bitstream/10183/259933/2/001169183.pdf.txtc3889a435f8940a2f74d397e2baf5a28MD52ORIGINAL001169183.pdfResumoapplication/pdf297208http://www.lume.ufrgs.br/bitstream/10183/259933/1/001169183.pdffab39582ed4bdf26a80e8297188b0985MD5110183/2599332023-07-05 03:49:15.552802oai:www.lume.ufrgs.br:10183/259933Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-07-05T06:49:15Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Streamflow estimation in ungauged catchments in Brazil using machine learning approaches
title Streamflow estimation in ungauged catchments in Brazil using machine learning approaches
spellingShingle Streamflow estimation in ungauged catchments in Brazil using machine learning approaches
Fontana, Rafael Barbedo
Escoamento : Simulacao
Bacias hidrográficas
Dados escassos
Aprendizado de máquina
title_short Streamflow estimation in ungauged catchments in Brazil using machine learning approaches
title_full Streamflow estimation in ungauged catchments in Brazil using machine learning approaches
title_fullStr Streamflow estimation in ungauged catchments in Brazil using machine learning approaches
title_full_unstemmed Streamflow estimation in ungauged catchments in Brazil using machine learning approaches
title_sort Streamflow estimation in ungauged catchments in Brazil using machine learning approaches
author Fontana, Rafael Barbedo
author_facet Fontana, Rafael Barbedo
Sorribas, Mino Viana
Collischonn, Walter
author_role author
author2 Sorribas, Mino Viana
Collischonn, Walter
author2_role author
author
dc.contributor.event.pt_BR.fl_str_mv European Geoscience Union. General Assembly. (2023 : Vienna : On-line)
dc.contributor.author.fl_str_mv Fontana, Rafael Barbedo
Sorribas, Mino Viana
Collischonn, Walter
dc.subject.por.fl_str_mv Escoamento : Simulacao
Bacias hidrográficas
Dados escassos
Aprendizado de máquina
topic Escoamento : Simulacao
Bacias hidrográficas
Dados escassos
Aprendizado de máquina
publishDate 2023
dc.date.accessioned.fl_str_mv 2023-07-04T03:51:07Z
dc.date.issued.fl_str_mv 2023
dc.type.driver.fl_str_mv Estrangeiro
info:eu-repo/semantics/conferenceObject
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10183/259933
dc.identifier.nrb.pt_BR.fl_str_mv 001169183
url http://hdl.handle.net/10183/259933
identifier_str_mv 001169183
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv European Geoscience Union. General Assembly (2023 : Vienna : On-line). [Programme]. [Göttingen : Copernicus, 2023]
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 Institucional da UFRGS
instname:Universidade Federal do Rio Grande do Sul (UFRGS)
instacron:UFRGS
instname_str Universidade Federal do Rio Grande do Sul (UFRGS)
instacron_str UFRGS
institution UFRGS
reponame_str Repositório Institucional da UFRGS
collection Repositório Institucional da UFRGS
bitstream.url.fl_str_mv http://www.lume.ufrgs.br/bitstream/10183/259933/2/001169183.pdf.txt
http://www.lume.ufrgs.br/bitstream/10183/259933/1/001169183.pdf
bitstream.checksum.fl_str_mv c3889a435f8940a2f74d397e2baf5a28
fab39582ed4bdf26a80e8297188b0985
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)
repository.mail.fl_str_mv
_version_ 1815447830834184192

Registros relacionados