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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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previous chapter Uncertainty Quantification in Data Fitting Neural and Hilbert Networks

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The process to compute the initial condition combining observations and the background data from the mathematical model is called data assimilation [13].

Radiation, turbulence, surface representation, cloud formation, rainfall processes.

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Title: Climate Precipitation Prediction with Uncertainty Quantification by Self-configuring Neural Network
Authors: Juliana A. Anochi
Reynier Hernández Torres
Haroldo F. Campos Velho
Publisher: Springer International Publishing
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