Climatic Data Neural Representation for Large Territorial Extensions: Case Study for the State of Minas Gerais

被引：0
|
作者：
Santos, Enock T. [1 ]
Zarate, Luis E. [1 ]
Pereira, Elizabeth M. D. [2 ]
机构：
[1] Pontificia Univ Catolica Minas Gerais, Appl Computat Intelligence Lab LICAP, Av Dom Jose Gaspar 500, BR-30535610 Belo Horizonte, MG, Brazil
[2] Pontificia Univ Catolica Minas Gerais, GREEN, BR-30535610 Belo Horizonte, MG, Brazil
来源：
IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6 | 2009年
关键词：
NETWORKS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 altitude.
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页码：2263 / +
页数：2
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