Open AccessNeural network for seasonal climate precipitation prediction on the Brazil
Author(s) -
Juliana Aparecida Anochi,
Haroldo Fraga de Campos Velho
Publication year - 2020
Publication title -
ciência e natura
Language(s) - English
Resource type - Journals
eISSN - 2179-460X
pISSN - 0100-8307
DOI - 10.5902/2179460x45358
Subject(s) - precipitation , artificial neural network , meteorology , climatology , perceptron , environmental science , multilayer perceptron , satellite , weather forecasting , computer science , machine learning , geography , geology , engineering , aerospace engineering
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).