%0 Conference Proceedings
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%@nexthigherunit 8JMKD3MGPCW/3ETL435
%@nexthigherunit 8JMKD3MGPCW/43SQKNE
%2 sid.inpe.br/mtc-m21c/2020/09.17.12.20.34
%4 sid.inpe.br/mtc-m21c/2020/09.17.12.20
%3 anochi_climate.pdf
%8 29 jun. - 03 jul.
%@issn 21954356
%A Anochi, Juliana Aparecida,
%A Torres, Reynier Hernández,
%A Campos Velho, Haroldo Fraga de,
%B International Symposium on Uncertainty Quantification and Stochastic Modelling, 5
%@secondarytype PRE CI
%C Rouen, France
%D 2020
%E Cursi, J. E. S.,
%@secondarykey INPE--PRE/
%I Springer
%K Neural network, Precipitation climate prediction, MPCA metaheuristic.
%O Lecture Notes in Mechanical Engineering
%P 242-253
%S Proceedings
%T Climate precipitation prediction with uncertainty quantification by self-configuring neural network
%X 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
%@electronicmailaddress juliana.anochi@inpe.br
%@electronicmailaddress elcio@ieav.cta.br
%@electronicmailaddress haroldo.camposvelho@inpe.br
%@documentstage not transferred
%@group DIDOP-CGCPT-INPE-MCTIC-GOV-BR
%@group COAMZ-CGOBT-INPE-MCTIC-GOV-BR
%@group LABAC-COCTE-INPE-MCTIC-GOV-BR
%@usergroup simone
%@isbn 978-303053668-8
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@resumeid
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JHC3
%@versiontype publisher
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}