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1. Identity statement Reference Type Conference Paper (Conference Proceedings) Site mtc-m21c.sid.inpe.br Holder Code isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S Identifier 8JMKD3MGP3W34R/439C6CE Repository sid.inpe.br/mtc-m21c/2020/09.17.12.20   (restricted access) Last Update 2020:09.17.12.20.34 (UTC) simone Metadata Repository sid.inpe.br/mtc-m21c/2020/09.17.12.20.34 Metadata Last Update 2022:01.04.01.35.24 (UTC) administrator Secondary Key INPE--PRE/ ISBN 978-303053668-8 ISSN 21954356 Citation Key AnochiTorrCamp:2020:ClPrPr Title Climate precipitation prediction with uncertainty quantification by self-configuring neural network Year 2020 Access Date 2024, May 17 Secondary Type PRE CI Number of Files 1 Size 2249 KiB 2. Context Author 1 Anochi, Juliana Aparecida 2 Torres, Reynier Hernández 3 Campos Velho, Haroldo Fraga de Resume Identifier 1 2 3 8JMKD3MGP5W/3C9JHC3 Group 1 DIDOP-CGCPT-INPE-MCTIC-GOV-BR 2 COAMZ-CGOBT-INPE-MCTIC-GOV-BR 3 LABAC-COCTE-INPE-MCTIC-GOV-BR Affiliation 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) Author e-Mail Address 1 juliana.anochi@inpe.br 2 elcio@ieav.cta.br 3 haroldo.camposvelho@inpe.br Editor Cursi, J. E. S. Conference Name International Symposium on Uncertainty Quantification and Stochastic Modelling, 5 Conference Location Rouen, France Date 29 jun. - 03 jul. Publisher Springer Pages 242-253 Book Title Proceedings History (UTC) 2020-09-17 12:20:34 :: simone -> administrator :: 2022-01-04 01:35:24 :: administrator -> simone :: 2020 3. Content and structure Is the master or a copy? is the master Content Stage completed Transferable 1 Content Type External Contribution Version Type publisher Keywords Neural network Precipitation climate prediction MPCA metaheuristic Abstract Artificial neural networks have been employed on many applications. Good results have been obtained by using neural network for the precipitation climate prediction to the Brazil. The input are some meteorological variables, as wind components for several levels, air temperature, and former precipitation. The neural network is automatically configured, by solving an optimization problem with Multi-Particle Collision Algorithm (MPCA) metaheuristic. However, it is necessary to address, beyond the prediction the uncertainty associated to the prediction. This paper is focused on two-fold. Firstly, to produce a monthly prediction for precipitation by neural network. Secondly, the neural network output is also designed to estimate the uncertainty related to neural prediction. Area MET Arrangement 1 urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Climate precipitation prediction... Arrangement 2 urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COAMZ > Climate precipitation prediction... Arrangement 3 urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDOP > Climate precipitation prediction... doc Directory Content access source Directory Content there are no files agreement Directory Content agreement.html 17/09/2020 09:20 1.0 KiB  4. Conditions of access and use Language en Target File anochi_climate.pdf User Group simone Visibility shown Read Permission deny from all and allow from 150.163 Update Permission not transferred 5. Allied materials Next Higher Units 8JMKD3MGPCW/3ESGTTP 8JMKD3MGPCW/3ETL435 8JMKD3MGPCW/43SQKNE Host Collection urlib.net/www/2017/11.22.19.04 6. Notes Notes Lecture Notes in Mechanical Engineering Empty Fields archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition format label lineage mark mirrorrepository nextedition numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisheraddress readergroup rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume 7. Description control e-Mail (login) simone update

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