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2021 | OriginalPaper | Chapter

Climate Precipitation Prediction with Uncertainty Quantification by Self-configuring Neural Network

Authors : Juliana A. Anochi, Reynier Hernández Torres, Haroldo F. Campos Velho

Published in: Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling

Publisher: Springer International Publishing

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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.

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Metadata
Title
Climate Precipitation Prediction with Uncertainty Quantification by Self-configuring Neural Network
Authors
Juliana A. Anochi
Reynier Hernández Torres
Haroldo F. Campos Velho
Copyright Year
2021
Book
Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling

Print ISBN: 978-3-030-53668-8

Electronic ISBN: 978-3-030-53669-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-53669-5_18