@InCollection{AnochiTorrCamp:2021:ClPrPr,
author = "Anochi, Juliana Aparecida and Torres, Reynier Hern{\'a}ndez and
Campos Velho, Haroldo Fraga de",
title = "Climate precipitation prediction with uncertainty quantification
by self-configuring neural network",
booktitle = "Proceedings of the 5th International Symposium on Uncertainty
Quantification and Stochastic Modelling",
publisher = "Springer",
year = "2021",
editor = "Cursi, J. E. S.",
pages = "242--253",
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.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
doi = "10.1007/978-3-030-53669-5_18",
url = "http://dx.doi.org/10.1007/978-3-030-53669-5_18",
isbn = "978-303053668-8",
language = "en",
targetfile = "anochi_climate.pdf",
urlaccessdate = "09 maio 2024"
}