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1. Identity statement
Reference TypeBook Section
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/43T9DRB
Repositorysid.inpe.br/mtc-m21c/2021/01.05.14.39   (restricted access)
Last Update2021:01.05.14.39.32 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2021/01.05.14.39.32
Metadata Last Update2022:04.04.04.50.15 (UTC) administrator
Secondary KeyINPE--/
DOI10.1007/978-3-030-53669-5_18
ISBN978-303053668-8
Citation KeyAnochiTorrCamp:2021:ClPrPr
TitleClimate precipitation prediction with uncertainty quantification by self-configuring neural network
Year2021
Access Date2024, May 17
Secondary TypePRE LI
Number of Files1
Size2328 KiB
2. Context
Author1 Anochi, Juliana Aparecida
2 Torres, Reynier Hernández
3 Campos Velho, Haroldo Fraga de
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHC3
Group1 DIPTC-CGCT-INPE-MCTI-GOV-BR
2 DIPE1-COGPI-INPE-MCTI-GOV-BR
3 COPDT-CGIP-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 juliana.anochi@inpe.br
2 reynier.torres@inpe.br
3 haroldo.camposvelho@inpe.br
EditorCursi, J. E. S.
Book TitleProceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling
PublisherSpringer
Pages242-253
History (UTC)2021-01-05 14:39:32 :: simone -> administrator ::
2022-04-04 04:50:15 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsNeural network · Precipitation climate prediction · MPCA metaheuristic
AbstractArtificial neural networks have been employed on many applications. Good results have been obtained by using neural network for the precipitation climate prediction to the Brazil. The input are some meteorological variables, as wind components for several levels, air temperature, and former precipitation. The neural network is automatically configured, by solving an optimization problem with Multi-Particle Collision Algorithm (MPCA) metaheuristic. However, it is necessary to address, beyond the prediction the uncertainty associated to the prediction. This paper is focused on two-fold. Firstly, to produce a monthly prediction for precipitation by neural network. Secondly, the neural network output is also designed to estimate the uncertainty related to neural prediction.
AreaMET
Arrangement 1urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Climate precipitation prediction...
Arrangement 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Climate precipitation prediction...
Arrangement 3urlib.net > BDMCI > Fonds > Produção a partir de 2021 > COGPI > Climate precipitation prediction...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
Languageen
Target Fileanochi_climate.pdf
User Groupsimone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUATE
8JMKD3MGPCW/46KUES5
8JMKD3MGPCW/46L2FGP
DisseminationBNDEPOSITOLEGAL
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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7. Description control
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