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Tipo da ReferênciaConference Proceedings
Identificador8JMKD3MGP3W34P/3M9LQ3E
Repositóriosid.inpe.br/mtc-m21b/2016/08.16.19.23
Metadadossid.inpe.br/mtc-m21b/2016/08.16.19.23.29
Sitemtc-m21b.sid.inpe.br
Chave SecundáriaINPE--PRE/
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Chave de CitaçãoRuivo:2016:DaMiAp
AutorRuivo, Heloisa Musetti
GrupoCST-CST-INPE-MCTI-GOV-BR
AfiliaçãoInstituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autorheloisa.ruivo@inpe.br
TítuloSevere precipitation evaluation in Brazil: Data mining approach
Nome do EventoInternational Conference on Integral Methods in Science and Engineering, 14 (IMSE)
Ano2016
Data25-29 July
Localização do EventoPadova, Italy
Palavras-ChaveSevere precipitation, statistical p-value analysis, decision tree algorithm.
ResumoData mining approach is applied to evaluate extreme rainfall events in the Brazil. Statistical analysis is combined with an artificial intelligence technique to identify the most relevant meteorological variables for a local severe precipitation in the Rio de Janeiro state (Brazil): Rio de Janeiro, and Nova Friburgo cities. The p-value statistical technique is employed to select a much smaller subset of climatic variables, preserving the information associated with extreme meteorological events. A decision tree algorithm is used as a model to identify the precipitation severity. The method is tested with the events at Apr/2009 (Rio de Janeiro city) and at Jan/2011 (Nova Friburgo city). In both cases, our results show a good local analysis for extreme precipition episodes.
Idiomaen
Tipo SecundárioPRE CI
AreaCST
Última Atualização dos Metadados2018:06.04.02.41.02 sid.inpe.br/mtc-m21b/2013/09.26.14.25.20 administrator {D 2016}
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e-Mail (login)simone
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Acervo Hospedeirosid.inpe.br/mtc-m21b/2013/09.26.14.25.20
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Unidades Imediatamente Superiores8JMKD3MGPCW/3F3T29H
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Histórico2016-08-16 19:23:45 :: simone -> administrator :: 2016
2018-06-04 02:41:02 :: administrator -> simone :: 2016
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Data de Acesso31 mar. 2020
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