Bayesian modeling of the maximum streamflows from the Furnas reservoir

Detalhes bibliográficos
Autor(a) principal: Costa,Matheus de Souza
Data de Publicação: 2022
Outros Autores: Beijo,Luiz Alberto, Avelar,Fabricio Goecking
Tipo de documento: Artigo
Idioma: por
Título da fonte: Engenharia Sanitaria e Ambiental
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-41522022000400693
Resumo: ABSTRACT The objective of this work was to predict the maximum flow of the Furnas reservoir in the dry and wet periods. The generalized distribution of extreme values (GEV) was used with parameter estimation via Bayesian inference. Data on average daily streamflows corresponding to the years 1965 to 2017 were obtained from Hidroweb, by the National Agency for Water and Basic Sanitation (Agência Nacional de Águas e Saneamento Básico – ANA), from which maximum values were extracted, by period and in each year. Accuracy and mean error of prediction of maximum streamflows were analyzed, comparing the estimates provided by the Bayesian inference, with informative and non-informative prior distributions. Information from a series of maximum streamflows from the Camargos reservoir was used to elicit the informative prior distribution. The use of prior information provided an increase in the precision and accuracy of the maximum streamflow estimates. Thus, the GEV model, with informative a priori distribution, was used to predict the return levels of Furnas maximum streamflow with their respective high posterior density intervals considering several return times.
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spelling Bayesian modeling of the maximum streamflows from the Furnas reservoiraccuracygeneralized distribution of extreme valuesinformative priorreturn levelsABSTRACT The objective of this work was to predict the maximum flow of the Furnas reservoir in the dry and wet periods. The generalized distribution of extreme values (GEV) was used with parameter estimation via Bayesian inference. Data on average daily streamflows corresponding to the years 1965 to 2017 were obtained from Hidroweb, by the National Agency for Water and Basic Sanitation (Agência Nacional de Águas e Saneamento Básico – ANA), from which maximum values were extracted, by period and in each year. Accuracy and mean error of prediction of maximum streamflows were analyzed, comparing the estimates provided by the Bayesian inference, with informative and non-informative prior distributions. Information from a series of maximum streamflows from the Camargos reservoir was used to elicit the informative prior distribution. The use of prior information provided an increase in the precision and accuracy of the maximum streamflow estimates. Thus, the GEV model, with informative a priori distribution, was used to predict the return levels of Furnas maximum streamflow with their respective high posterior density intervals considering several return times.Associação Brasileira de Engenharia Sanitária e Ambiental - ABES2022-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-41522022000400693Engenharia Sanitaria e Ambiental v.27 n.4 2022reponame:Engenharia Sanitaria e Ambientalinstname:Associação Brasileira de Engenharia Sanitária e Ambiental (ABES)instacron:ABES10.1590/s1413-415220200177info:eu-repo/semantics/openAccessCosta,Matheus de SouzaBeijo,Luiz AlbertoAvelar,Fabricio Goeckingpor2022-08-15T00:00:00Zoai:scielo:S1413-41522022000400693Revistahttp://www.scielo.br/esaONGhttps://old.scielo.br/oai/scielo-oai.php||esa@abes-dn.org.br1809-44571413-4152opendoar:2022-08-15T00:00Engenharia Sanitaria e Ambiental - Associação Brasileira de Engenharia Sanitária e Ambiental (ABES)false
dc.title.none.fl_str_mv Bayesian modeling of the maximum streamflows from the Furnas reservoir
title Bayesian modeling of the maximum streamflows from the Furnas reservoir
spellingShingle Bayesian modeling of the maximum streamflows from the Furnas reservoir
Costa,Matheus de Souza
accuracy
generalized distribution of extreme values
informative prior
return levels
title_short Bayesian modeling of the maximum streamflows from the Furnas reservoir
title_full Bayesian modeling of the maximum streamflows from the Furnas reservoir
title_fullStr Bayesian modeling of the maximum streamflows from the Furnas reservoir
title_full_unstemmed Bayesian modeling of the maximum streamflows from the Furnas reservoir
title_sort Bayesian modeling of the maximum streamflows from the Furnas reservoir
author Costa,Matheus de Souza
author_facet Costa,Matheus de Souza
Beijo,Luiz Alberto
Avelar,Fabricio Goecking
author_role author
author2 Beijo,Luiz Alberto
Avelar,Fabricio Goecking
author2_role author
author
dc.contributor.author.fl_str_mv Costa,Matheus de Souza
Beijo,Luiz Alberto
Avelar,Fabricio Goecking
dc.subject.por.fl_str_mv accuracy
generalized distribution of extreme values
informative prior
return levels
topic accuracy
generalized distribution of extreme values
informative prior
return levels
description ABSTRACT The objective of this work was to predict the maximum flow of the Furnas reservoir in the dry and wet periods. The generalized distribution of extreme values (GEV) was used with parameter estimation via Bayesian inference. Data on average daily streamflows corresponding to the years 1965 to 2017 were obtained from Hidroweb, by the National Agency for Water and Basic Sanitation (Agência Nacional de Águas e Saneamento Básico – ANA), from which maximum values were extracted, by period and in each year. Accuracy and mean error of prediction of maximum streamflows were analyzed, comparing the estimates provided by the Bayesian inference, with informative and non-informative prior distributions. Information from a series of maximum streamflows from the Camargos reservoir was used to elicit the informative prior distribution. The use of prior information provided an increase in the precision and accuracy of the maximum streamflow estimates. Thus, the GEV model, with informative a priori distribution, was used to predict the return levels of Furnas maximum streamflow with their respective high posterior density intervals considering several return times.
publishDate 2022
dc.date.none.fl_str_mv 2022-08-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-41522022000400693
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-41522022000400693
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv 10.1590/s1413-415220200177
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Engenharia Sanitária e Ambiental - ABES
publisher.none.fl_str_mv Associação Brasileira de Engenharia Sanitária e Ambiental - ABES
dc.source.none.fl_str_mv Engenharia Sanitaria e Ambiental v.27 n.4 2022
reponame:Engenharia Sanitaria e Ambiental
instname:Associação Brasileira de Engenharia Sanitária e Ambiental (ABES)
instacron:ABES
instname_str Associação Brasileira de Engenharia Sanitária e Ambiental (ABES)
instacron_str ABES
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reponame_str Engenharia Sanitaria e Ambiental
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repository.name.fl_str_mv Engenharia Sanitaria e Ambiental - Associação Brasileira de Engenharia Sanitária e Ambiental (ABES)
repository.mail.fl_str_mv ||esa@abes-dn.org.br
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