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1 referência encontrada buscando em 15 dentre 15 sites.
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Tipo da ReferênciaJournal Article
Identificador8JMKD3MGP3W/3MTN65S
Repositóriosid.inpe.br/plutao/2016/12.05.19.53.48
Metadadossid.inpe.br/plutao/2016/12.05.19.53.49
Siteplutao.sid.inpe.br
DOI10.5923/s.ajee.201601.14
Rótulolattes: 2720072834057575 1 AnochiCamp:2016:MePrCl
ISSN2166-4633
2166-465X
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Chave de CitaçãoAnochiCamp:2016:MePrCl
Autor1 Anochi, Juliana Aparecida
2 Campos Velho, Haroldo Fraga de
Identificador de Curriculo1
2 8JMKD3MGP5W/3C9JHC3
Grupo1 DOP-CPT-INPE-MCTI-GOV-BR
2 LAC-CTE-INPE-MCTI-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 juliana.anochi@inpe.br
2 haroldo.camposvelho@inpe.br
TítuloMesoscale precipitation climate prediction for brazilian south region by artificial neural networks
RevistaAmerican Journal of Environmental Engineering
Ano2016
Volume6
Número4
Palavras-ChaveClimate prediction, Precipitation, Self-configured neural network, Data reduction.
ResumoNumerical weather and climate use sophisticated mathematical models. These models are employed to simulate the atmospheric dynamics to perform a medium-range forecasting and climate prediction. Such an approach allows to estimate all meteorological variables for a future time period: wind fields, air temperature, pressure, moisture, and precipitation field. Precipitation is one of the most difficult fields for prediction. The latter statement is verified due to high variability in space and time. However, precipitation is a key issue in many activities of society. An alternative approach for climate prediction to the precipitation field is to employ the Artificial Neural Network (ANN). Such technique has a reduced computational cost in comparison with time integration of the partial differential equations. One challenge to employ an ANN is to determine the topology or configuration of a neural network. Here, a supervised ANN is designed to perform the precipitation prediction looking at two different periods: monthly and seasonal precipitation. The method is applied to the Southern region of Brazil. The definition of the neural network topology is addressed as an optimization problem. The best configuration is computed by minimizing a cost function. The optimization problem is solved by a new meta-heuristic: Multi-Particle Collision Algorithm (MPCA). In addition, a technique based on rough set theory is used to reduce the data space dimension. The predicted precipitation is evaluated by comparison with measured data. The prediction is also evaluated using full and reduced input data for a neural predictive model.
Páginas94-102
Idiomaen
URL (dados não confiáveis)http://article.sapub.org/10.5923.s.ajee.201601.14.html
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
AreaCOMP
Nota SecundáriaB3_GEOCIÊNCIAS B3_ENGENHARIAS_II B4_ENGENHARIAS_III
Nota Terciária8JMKD3MGP3W34P/3K98PDP
Tamanho993 KiB
Número de Arquivos1
Última Atualização2016:12.07.11.27.10 dpi.inpe.br/plutao@80/2008/08.19.15.01 administrator
Última Atualização dos Metadados2018:06.04.23.26.26 dpi.inpe.br/plutao@80/2008/08.19.15.01 administrator {D 2016}
Estágio do Documentoconcluido
É a matriz ou uma cópia?é a matriz
Espelhourlib.net/www/2011/03.29.20.55
e-Mail (login)simone
Grupo de Usuárioslattes
self-uploading-INPE-MCTI-GOV-BR
Grupo de Leitoresadministrator
lattes
Visibilidadeshown
Transferível1
Acervo Hospedeirodpi.inpe.br/plutao@80/2008/08.19.15.01
Tipo do ConteudoExternal Contribution
Estágio do Documentonot transferred
Tipo de Versãopublisher
Política de Arquivamentoallowpublisher allowfinaldraft
Permissão de Leituraallow from all
Unidades Imediatamente Superiores8JMKD3MGPCW/3ESGTTP
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreementnão têm arquivos
Histórico2016-12-05 19:53:49 :: lattes -> administrator ::
2016-12-07 03:44:30 :: administrator -> lattes :: 2016
2016-12-07 11:27:11 :: lattes -> administrator :: 2016
2018-06-04 23:26:26 :: administrator -> simone :: 2016
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress format isbn lineage mark month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder secondarydate secondarykey session shorttitle sponsor subject targetfile tertiarytype
Data de Acesso19 out. 2020
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