Bayesian modeling of the maximum streamflows from the Furnas reservoir
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
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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Engenharia Sanitaria e Ambiental |
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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 |
institution |
ABES |
reponame_str |
Engenharia Sanitaria e Ambiental |
collection |
Engenharia Sanitaria e Ambiental |
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 |
_version_ |
1754213197775110144 |