Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Brasileira de Meteorologia (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862017000400575 |
Resumo: | Abstract Modeling by multiple enchained imputation is an area of growing importance. However, its models and methods are frequently developed for specific applications. In this study the model for multiple imputation was used to estimate daily rainfall data. Daily precipitation records from several meteorological stations were used, obtained from system AGRITEMPO for two homogenous climatic zones. The precipitation values obtained for two dates (Jan. 20th 2005 and May 2nd 2005) using the multiple imputation model were compared with geo-statistics techniques ordinary Kriging and Co-kriging with the altitude as an auxiliary variable. The multiple imputation model was 16% better for the first zone and over 23% for the second one, compared to the rainfall estimation obtained by geo-statistical techniques. The model proved to be a versatile technique, presenting coherent results with the conditions of different zones and times. |
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Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faultsmodel by multiple imputationchainsprecipitationordinary krigingordinary Co-kriginghomogeneous zonesAbstract Modeling by multiple enchained imputation is an area of growing importance. However, its models and methods are frequently developed for specific applications. In this study the model for multiple imputation was used to estimate daily rainfall data. Daily precipitation records from several meteorological stations were used, obtained from system AGRITEMPO for two homogenous climatic zones. The precipitation values obtained for two dates (Jan. 20th 2005 and May 2nd 2005) using the multiple imputation model were compared with geo-statistics techniques ordinary Kriging and Co-kriging with the altitude as an auxiliary variable. The multiple imputation model was 16% better for the first zone and over 23% for the second one, compared to the rainfall estimation obtained by geo-statistical techniques. The model proved to be a versatile technique, presenting coherent results with the conditions of different zones and times.Sociedade Brasileira de Meteorologia2017-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862017000400575Revista Brasileira de Meteorologia v.32 n.4 2017reponame:Revista Brasileira de Meteorologia (Online)instname:Sociedade Brasileira de Meteorologia (SBMET)instacron:SBMET10.1590/0102-7786324006info:eu-repo/semantics/openAccessde Carvalho,José Ruy PortoAlmeida Monteiro,José Eduardo BoffinhoNakai,Alan MassaruAssad,Eduardo Delgadoeng2019-05-27T00:00:00Zoai:scielo:S0102-77862017000400575Revistahttp://www.rbmet.org.br/port/index.phpONGhttps://old.scielo.br/oai/scielo-oai.php||rbmet@rbmet.org.br1982-43510102-7786opendoar:2019-05-27T00:00Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)false |
dc.title.none.fl_str_mv |
Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults |
title |
Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults |
spellingShingle |
Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults de Carvalho,José Ruy Porto model by multiple imputation chains precipitation ordinary kriging ordinary Co-kriging homogeneous zones |
title_short |
Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults |
title_full |
Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults |
title_fullStr |
Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults |
title_full_unstemmed |
Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults |
title_sort |
Model for Multiple Imputation to Estimate Daily Rainfall Data and Filling of Faults |
author |
de Carvalho,José Ruy Porto |
author_facet |
de Carvalho,José Ruy Porto Almeida Monteiro,José Eduardo Boffinho Nakai,Alan Massaru Assad,Eduardo Delgado |
author_role |
author |
author2 |
Almeida Monteiro,José Eduardo Boffinho Nakai,Alan Massaru Assad,Eduardo Delgado |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
de Carvalho,José Ruy Porto Almeida Monteiro,José Eduardo Boffinho Nakai,Alan Massaru Assad,Eduardo Delgado |
dc.subject.por.fl_str_mv |
model by multiple imputation chains precipitation ordinary kriging ordinary Co-kriging homogeneous zones |
topic |
model by multiple imputation chains precipitation ordinary kriging ordinary Co-kriging homogeneous zones |
description |
Abstract Modeling by multiple enchained imputation is an area of growing importance. However, its models and methods are frequently developed for specific applications. In this study the model for multiple imputation was used to estimate daily rainfall data. Daily precipitation records from several meteorological stations were used, obtained from system AGRITEMPO for two homogenous climatic zones. The precipitation values obtained for two dates (Jan. 20th 2005 and May 2nd 2005) using the multiple imputation model were compared with geo-statistics techniques ordinary Kriging and Co-kriging with the altitude as an auxiliary variable. The multiple imputation model was 16% better for the first zone and over 23% for the second one, compared to the rainfall estimation obtained by geo-statistical techniques. The model proved to be a versatile technique, presenting coherent results with the conditions of different zones and times. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-12-01 |
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=S0102-77862017000400575 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862017000400575 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0102-7786324006 |
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 |
Sociedade Brasileira de Meteorologia |
publisher.none.fl_str_mv |
Sociedade Brasileira de Meteorologia |
dc.source.none.fl_str_mv |
Revista Brasileira de Meteorologia v.32 n.4 2017 reponame:Revista Brasileira de Meteorologia (Online) instname:Sociedade Brasileira de Meteorologia (SBMET) instacron:SBMET |
instname_str |
Sociedade Brasileira de Meteorologia (SBMET) |
instacron_str |
SBMET |
institution |
SBMET |
reponame_str |
Revista Brasileira de Meteorologia (Online) |
collection |
Revista Brasileira de Meteorologia (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET) |
repository.mail.fl_str_mv |
||rbmet@rbmet.org.br |
_version_ |
1752122085715476480 |