1. Identity statement | |
Reference Type | Journal Article |
Site | mtc-m16b.sid.inpe.br |
Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
Identifier | x6e6X3pFwXQZ3DUS8rS5/FfUC9 |
Repository | cptec.inpe.br/walmeida/2005/02.23.10.20 (restricted access) |
Last Update | 2005:02.23.03.00.00 (UTC) administrator |
Metadata Repository | cptec.inpe.br/walmeida/2005/02.23.10.20.04 |
Metadata Last Update | 2021:02.10.19.21.31 (UTC) administrator |
Secondary Key | INPE-12422-PRE/7726 |
ISSN | 0022-1694 |
Citation Key | ValverdeRamirezCampFerr:2005:ArNeNe |
Title | Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region |
Year | 2005 |
Month | Jan. |
Access Date | 2024, May 11 |
Secondary Type | PRE PI |
Number of Files | 1 |
Size | 519 KiB |
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2. Context | |
Author | 1 Valverde Ramirez, Maria Cleofe 2 Campos Velho, Haroldo Fraga de 3 Ferreira, Nelson Jesus |
Resume Identifier | 1 2 8JMKD3MGP5W/3C9JHC3 3 8JMKD3MGP5W/3C9JHUB |
Group | 1 DOP-INPE-MCT-BR |
Affiliation | 1 CPTEC-INPE-Cachoeira Paulista-12630-000-SP-Brasil |
e-Mail Address | atus@cptec.inpe.br |
Journal | Journal of Hydrology |
Volume | 301 |
Number | 1-4 |
Pages | 146-162 |
History (UTC) | 2005-05-12 14:31:31 :: fabia -> administrator :: 2008-06-10 19:51:38 :: administrator -> estagiario :: 2010-05-11 16:55:40 :: estagiario -> administrator :: 2021-02-10 19:21:31 :: administrator -> marciana :: 2005 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | artificial neural network statistical rainfall forecast multiple linear regression Regional ETA model |
Abstract | An 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 1997/2002 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. |
Area | MET |
Arrangement | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDOP > Artificial neural network... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | there are no files |
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4. Conditions of access and use | |
Language | en |
Target File | Ramirez_Artificial neural network.pdf.pdf |
User Group | administrator fabia |
Visibility | shown |
Copy Holder | SID/SCD |
Archiving Policy | denypublisher denyfinaldraft24 |
Read Permission | deny from all and allow from 150.163 |
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5. Allied materials | |
Next Higher Units | 8JMKD3MGPCW/43SQKNE |
Dissemination | WEBSCI; PORTALCAPES; MGA; COMPENDEX. |
Host Collection | cptec.inpe.br/walmeida/2003/04.25.17.12 |
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6. Notes | |
Empty Fields | alternatejournal archivist callnumber copyright creatorhistory descriptionlevel documentstage doi electronicmailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarymark session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url versiontype |
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7. Description control | |
e-Mail (login) | marciana |
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