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| Registros recuperados : 2 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
21/12/2017 |
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Data da última atualização: |
21/12/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
CARVALHO, J. R. P. de; MONTEIRO, J. E. B. de A.; NAKAI, A. M.; ASSAD, E. D. |
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Afiliação: |
JOSE RUY PORTO DE CARVALHO, CNPTIA; JOSE EDUARDO B DE ALMEIDA MONTEIRO, CNPTIA; ALAN MASSARU NAKAI, CNPTIA; EDUARDO DELGADO ASSAD, CNPTIA. |
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Título: |
Model for multiple imputation to estimate daily rainfall data and filling of faults. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Revista Brasileira de Meteorologia, São Paulo, v. 32, n. 4, 575-583, Oct./Dec. 2017. |
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DOI: |
http://dx.doi.org/10.1590/0102-7786324006 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: José Eduardo Boffinho Almeida Monteiro. |
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Conteúdo: |
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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Palavras-Chave: |
Geoestatística; Homogeneous zones; Missing data; Model by multiple imputation; Modelagem por imputação múltipla; Ordinary Co-kriging; Ordinary kriging; Precipitação; Precipitation; Sistema Agritempo; Statistical inference. |
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Thesaurus NAL: |
Geostatistics; Meteorological data. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/169478/1/AP-Model-rbmet.pdf
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Marc: |
LEADER 02047naa a2200337 a 4500
001 2083153
005 2017-12-21
008 2017 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1590/0102-7786324006$2DOI
100 1 $aCARVALHO, J. R. P. de
245 $aModel for multiple imputation to estimate daily rainfall data and filling of faults.$h[electronic resource]
260 $c2017
500 $aNa publicação: José Eduardo Boffinho Almeida Monteiro.
520 $aModeling 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.
650 $aGeostatistics
650 $aMeteorological data
653 $aGeoestatística
653 $aHomogeneous zones
653 $aMissing data
653 $aModel by multiple imputation
653 $aModelagem por imputação múltipla
653 $aOrdinary Co-kriging
653 $aOrdinary kriging
653 $aPrecipitação
653 $aPrecipitation
653 $aSistema Agritempo
653 $aStatistical inference
700 1 $aMONTEIRO, J. E. B. de A.
700 1 $aNAKAI, A. M.
700 1 $aASSAD, E. D.
773 $tRevista Brasileira de Meteorologia, São Paulo$gv. 32, n. 4, 575-583, Oct./Dec. 2017.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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