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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier6qtX3pFwXQZ3r59YDa/JsESM
Repositorysid.inpe.br/iris@1916/2005/12.14.15.17   (restricted access)
Last Update2006:01.17.12.59.00 (UTC) administrator
Metadata Repositorysid.inpe.br/iris@1916/2005/12.14.15.17.45
Metadata Last Update2021:02.10.19.21.14 (UTC) administrator
Secondary KeyINPE-13427-PRE/8640
ISSN0022-1694
Citation KeyRamirezCampFerr:2005:ArNeNe
TitleArtificial Neural Network Technique for Precipitation Forecasts Applied to the Sao Paulo Region
Year2005
Secondary Date20060117
Access Date2024, Apr. 28
Secondary TypePRE PI
Number of Files1
Size519 KiB
2. Context
Author1 Ramirez, Maria Cleofé Valverde
2 Campos Velho, Haroldo Fraga de
3 Ferreira, Nelson Jesus
Resume Identifier1
2 8JMKD3MGP5W/3C9JHC3
3 8JMKD3MGP5W/3C9JHUB
Group1 LAC-INPE-MCT-BR
2 DOP-INPE-MCT-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais, Laboratório Associado de Computação e Matemática  Aplicada, (INPE, LAC)/CPTEC
e-Mail Addressatus@cptec.inpe.br
JournalJournal of Hydrology
Volume301
Number1-4
Pages146-162
History (UTC)2006-02-06 11:51:06 :: simone -> administrator ::
2021-02-10 19:21:14 :: administrator -> marciana :: 2005
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsArtificial neural network
Statistical rainfall forecast
Multiple linear regression
Regional ETA model
AbstractAn artificial neural network (ANN) technique is used to construct a nonlinear mapping between output data from a regional ETA model ran at the Center for Weather Forecasts and Climate Studies/National Institute for Space Research/Brazil, and surface rainfall data for the region of São Paulo State, Brazil. The objective is to generate site-specific quantitative forecasts of daily rainfall. The test was performed for six locations in São Paulo State during the austral summer and winter of the 19972002 period. The analysis was made using a feedforward neural network and resilient propagation learning algorithm. Meteorological variables from the ETA model (potential temperature, vertical component of the wind, specific humidity, air temperature, precipitable water, relative vorticity and moisture divergence flux) are used as input data to the trained networks, which generate rainfall forecast for the next time step. Additionally, predictions with a multiple linear regression model were compared to those of ANN. In order to evaluate the rainfall forecast skill over the studied region a statistical analysis was performed. The results show that ANN forecasts were superior to the ones obtained by the linear regression model thus revealing a great potential for an operational suite..
AreaMET
Arrangement 1urlib.net > LABAC > Artificial Neural Network...
Arrangement 2Artificial Neural Network...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
Languageen
Target FileRamirez_Ferreira_ArtificialNeural.pdf
User Groupadministrator
simone
Visibilityshown
Copy HolderSID/SCD
Archiving Policydenypublisher denyfinaldraft24
Read Permissiondeny from all and allow from 150.163
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/43SQKNE
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX.
Host Collectionsid.inpe.br/banon/2003/08.15.17.40
6. Notes
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7. Description control
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