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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W/3JJPDUT
Repositorysid.inpe.br/plutao/2015/06.01.12.55.46   (restricted access)
Last Update2015:07.06.17.35.55 (UTC) administrator
Metadata Repositorysid.inpe.br/plutao/2015/06.01.12.55.47
Metadata Last Update2018:06.04.23.25.37 (UTC) administrator
DOI10.17265/2159-5291/2015.05.005
ISSN2159-5291
Labellattes: 2720072834057575 1 AnochiCamp:2015:ClPrPr
Citation KeyAnochiCamp:2015:ClPrPr
TitleClimate precipitation prediction by neural network
Year2015
Access Date2024, May 17
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size3511 KiB
2. Context
Author1 Anochi, Juliana Aparecida
2 Campos Velho, Haroldo Fraga de
Resume Identifier1
2 8JMKD3MGP5W/3C9JHC3
Group1 CAP-COMP-SPG-INPE-MCTI-GOV-BR
2 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 juliana.anochi@gmail.com
2 haroldo@lac.inpe.br
JournalJournal of Mathematics and System Science
Volume5
Pages207-213
History (UTC)2015-06-01 13:34:00 :: lattes -> administrator :: 2015
2018-06-04 23:25:37 :: administrator -> simone :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsClimate Prediction
Neural Networks
Rough Sets Theory
AbstractIn this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.
AreaCOMP
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Climate precipitation prediction...
Arrangement 2urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > Climate precipitation prediction...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languagept
User Grouplattes
simone
Reader Groupadministrator
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Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/3F2PHGS
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.49.40 3
sid.inpe.br/bibdigital/2013/10.12.22.16 1
sid.inpe.br/bibdigital/2013/09.22.23.14 1
URL (untrusted data)http://www.davidpublisher.com/Home/Journal/JMSS
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
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