%0 Journal Article
%4 sid.inpe.br/plutao/2015/06.01.12.55.46
%2 sid.inpe.br/plutao/2015/06.01.12.55.47
%@doi 10.17265/2159-5291/2015.05.005
%@issn 2159-5291
%F lattes: 2720072834057575 1 AnochiCamp:2015:ClPrPr
%T Climate precipitation prediction by neural network
%D 2015
%9 journal article
%A Anochi, Juliana Aparecida,
%A Campos Velho, Haroldo Fraga de,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress juliana.anochi@gmail.com
%@electronicmailaddress haroldo@lac.inpe.br
%B Journal of Mathematics and System Science
%V 5
%P 207-213
%K Climate Prediction, Neural Networks, Rough Sets Theory.
%X 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.
%@language pt
%U http://www.davidpublisher.com/Home/Journal/JMSS