@Article{AnochiCamp:2015:ClPrPr,
author = "Anochi, Juliana Aparecida and Campos Velho, Haroldo Fraga de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Climate precipitation prediction by neural network",
journal = "Journal of Mathematics and System Science",
year = "2015",
volume = "5",
pages = "207--213",
keywords = "Climate Prediction, Neural Networks, Rough Sets Theory.",
abstract = "In this work a neural network model for climate forecasting is
presented. The model is built by training a neural network with
available reanalysis data. In order to assess the model, the
development methodology considers the use of data reduction
strategies that eliminate data redundancy thus reducing the
complexity of the models. The results presented in this paper
considered the use of Rough Sets Theory principles in extracting
relevant information from the available data to achieve the
reduction of redundancy among the variables used for forecasting
purposes. The paper presents results of climate prediction made
with the use of the neural network based model. The results
obtained in the conducted experiments show the effectiveness of
the methodology, presenting estimates similar to observations.",
doi = "10.17265/2159-5291/2015.05.005",
url = "http://dx.doi.org/10.17265/2159-5291/2015.05.005",
issn = "2159-5291",
label = "lattes: 2720072834057575 1 AnochiCamp:2015:ClPrPr",
language = "pt",
url = "http://www.davidpublisher.com/Home/Journal/JMSS",
urlaccessdate = "28 mar. 2024"
}