%0 Book Section
%@nexthigherunit 8JMKD3MGPCW/46KUATE
%@nexthigherunit 8JMKD3MGPCW/46KUES5
%@nexthigherunit 8JMKD3MGPCW/46L2FGP
%2 sid.inpe.br/mtc-m21c/2021/01.05.14.39.32
%4 sid.inpe.br/mtc-m21c/2021/01.05.14.39
%3 anochi_climate.pdf
%A Anochi, Juliana Aparecida,
%A Torres, Reynier Hernández,
%A Campos Velho, Haroldo Fraga de,
%@secondarytype PRE LI
%B Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling
%D 2021
%E Cursi, J. E. S.,
%@secondarykey INPE--/
%I Springer
%K Neural network · Precipitation climate prediction · MPCA metaheuristic.
%P 242-253
%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 reynier.torres@inpe.br
%@electronicmailaddress haroldo.camposvelho@inpe.br
%@group DIPTC-CGCT-INPE-MCTI-GOV-BR
%@group DIPE1-COGPI-INPE-MCTI-GOV-BR
%@group COPDT-CGIP-INPE-MCTI-GOV-BR
%@dissemination BNDEPOSITOLEGAL
%@isbn 978-303053668-8
%@usergroup simone
%@resumeid
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JHC3
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@versiontype publisher
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@doi 10.1007/978-3-030-53669-5_18