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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/42PR5PL
Repositorysid.inpe.br/mtc-m21c/2020/07.02.12.12   (restricted access)
Last Update2020:07.02.12.12.16 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/07.02.12.12.16
Metadata Last Update2022:01.04.01.35.15 (UTC) administrator
DOI10.1007/s00024-019-02386-y
ISSN0033-4553
Citation KeyAnochiTorrCamp:2020:TwGeAp
TitleTwo geoscience applications by optimal neural network architecture
Year2020
MonthJune
Access Date2024, May 17
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size4383 KiB
2. Context
Author1 Anochi, Juliana Aparecida
2 Torres, Reynier Hernández
3 Campos Velho, Haroldo Fraga de
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHC3
ORCID1 0000-0003-0769-9750
2 0000-0003-1554-5145
3 0000-0003-4968-5330
Group1 DIDOP-CGCPT-INPE-MCTIC-GOV-BR
2 COAMZ-CGOBT-INPE-MCTIC-GOV-BR
3 LABAC-COCTE-INPE-MCTIC-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
JournalPure and Applied Geophysics
Volume177
Number6
Pages2663-268
Secondary MarkA2_GEOCIÊNCIAS A2_CIÊNCIAS_AGRÁRIAS_I B1_BIODIVERSIDADE B2_CIÊNCIAS_BIOLÓGICAS_I B3_ASTRONOMIA_/_FÍSICA
History (UTC)2020-07-02 12:12:16 :: simone -> administrator ::
2020-07-02 12:12:17 :: administrator -> simone :: 2020
2020-07-02 12:12:38 :: simone -> administrator :: 2020
2022-01-04 01:35:15 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsMetaheuristics
optimization problem
neural network
data assimilation
climate precipitation prediction
monoobjective problem
multi-objective problem
AbstractNowadays, artificial neural networks have been successfully applied on several research and application fields. An appropriate configuration for a neural network is a complex task, and it often requires the knowledge of an expert on the application. A technique for automatic configuration for a neural network is formulated as an optimization problem. Two strategies are considered: a mono-objective minimization problem, using multiparticle collision algorithm (MPCA); and a multi-objective minimization problem addressed by the non-dominated sorting genetic algorithm (NSGA-II). The proposed optimization approaches were tested for two application in geosciences: data assimilation for wave evolution equation, and the mesoscale seasonal climate prediction for precipitation. Better results with automatic configuration were obtained for data assimilation than those obtained by network defined by an expert. For climate seasonal precipitation, automatic configuration presented better predictions were presented than ones carried out by an expert. For the worked examples, the NSGA-II presented a superior result for the worked experiments.
AreaMET
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4. Conditions of access and use
Languageen
Target Fileanochi_two.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher denyfinaldraft12
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/3ETL435
8JMKD3MGPCW/43SQKNE
Citing Item Listsid.inpe.br/bibdigital/2013/09.22.23.14 3
sid.inpe.br/mtc-m21/2012/07.13.14.49.40 2
sid.inpe.br/bibdigital/2013/09.29.19.53 2
DisseminationWEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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