Climatic data neural representation for large territorial extensions: case study for the state of Minas Gerais

Authors:
Enock T. Santos;Luis E. Zárate;Elizabeth M. D. Pereira
Affiliations:
Applied Computational Intelligence Laboratory, Pontifical Catholic University of Minas Gerais, Belo Horizonte, MG, Brasil;Applied Computational Intelligence Laboratory, Pontifical Catholic University of Minas Gerais, Belo Horizonte, MG, Brasil;Pontifical Catholic University of Minas Gerais, Belo Horizonte, MG, Brasil
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

it is possible to observe that for large areas the number of meteorological stations is small or they are improperly distributed. In environments or systems whose climatic variables impact directly or indirectly in the production, it is necessary to know or at least be able to estimate climate data to improve the production of the processes. To meet this demand, in this paper a representation of weather data for large areas through artificial neural networks (ANN) is proposed. All the procedures adopted are detailed which allow to be used to represent other regions. The main input variables of the neural model are the latitude, longitude and attitude.