Neural network for seasonal climate precipitation prediction on the Brazil

Authors

DOI:

https://doi.org/10.5902/2179460X45358

Keywords:

Precipitation, Seasonal climate prediction, Self-configured neural network

Abstract

Precipitation is the hardest meteorological field to be predicted. An approach based on and optimal neural network is applied for climate precipitation prediction for the Brazil. A self-configurated multi-layer perceptron neural network (MLP-NN) is used as a predictor tool. The MLP-NN topology is found by solving an optimization problem by the Multi-Particle Collision Algorithm (MPCA). Prediction for Summer and Winter seasons are shown. The neural forecasting is evaluated by using the reanalysis data from the NCEP/NCAR and data from satellite GPCP (Global Precipitation Climatology Project -- monthly precipitation dataset).

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Author Biographies

Juliana Aparecida Anochi, Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP

Graduated in Computer Science, master's degree in Applied Computing from the National Institute for Space Research (INPE) and doctorate in Applied Computing from the INPE on the theme of climate precipitation prediction by a neural network

Haroldo Fraga de Campos Velho, Instituto Nacional de Pesquisas Espaciais, São Jose dos Campos, SP

Graduated in Chemical Engineering from the Pontifica Universidade Católica do Rio Grande do Sul, M.Sc. on Nuclear Engineering and D.Sc. on Mechanical Engineering from the Universidade  Federal do Rio Grande do Sul. Currently, he is a senior researcher from the National Institute of Space Research (INPE, Brazil)

References

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Published

2020-08-28

How to Cite

Anochi, J. A., & Velho, H. F. de C. (2020). Neural network for seasonal climate precipitation prediction on the Brazil. Ciência E Natura, 42, e15. https://doi.org/10.5902/2179460X45358

Issue

Vol. 42 (2020): SPECIAL EDITION: XI WORKSHOP BRASILEIRO DE MICROMETEOROLOGIA

Section

Special Edition

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