APLICAÇÃO DE REDES NEURAIS ARTIFICIAIS NO TRATAMENTO DE DADOS AGROMETEOROLÓGICOS VISANDO A CORREÇÃO DE SÉRIES TEMPORAIS

• Ano de defesa: 2012
• Autor(a) principal: Viecheneski, Rodrigo
• Orientador(a): Mathias, Ivo Mario
• Banca de defesa: Herai, Roberto Hirochi , Dias, Ariangelo Hauer
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: UNIVERSIDADE ESTADUAL DE PONTA GROSSA
• Programa de Pós-Graduação: Programa de Pós Graduação Computação Aplicada
• Departamento: Computação para Tecnologias em Agricultura
• País: BR
• Palavras-chave em Português: Sistema Computacional; perceptron de múltiplas camadas; Variáveis Agrometeorológicas; Backpropagation
• Palavras-chave em Inglês: Computational System; multilayer perceptron; Agrometeorological Variables; Backpropagation
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.uepg.br/jspui/handle/prefix/157

This dissertation presents the development of a computational system called System for Treatment of Agrometeorological weather Series (STST Agrometeorológicas), with the objective of treating agrometeorological data in order to correct time weather. For the development of the study some data were collected from the agrometeorological stations, provided by Fundação ABC. The stations were located in the state of Paraná, in the cities of Ponta Grossa (long - 49.95025733, lat - 25.30156819) and Castro (long -49.8672, lat -24.6752). The computational system that has been suggested made use of the technology of Artificial Neural Networks on the type of Multilayer Perceptron and the backpropagation training algorithm of backpropagation error. It was developed with the Object Pascal programming language, using the integrated development environment Embarcadero Delphi 2009. To validate the proposed method we conducted six case studies, and the one which presented the best result for agrometeorological variable average temperature was the first case study of Castro's weather station, with a hit percentage between the treated registers and the registers without failure of 96.5%, a Pearson correlation coefficient of 0.98 and a simple average of the errors obtained from the training the neural network of 0.026406. The average errors of the neural networks was calculated between the values of errors obtained in each training during a period of correction failure. For the agrometeorological variable relative humidity, the best result was found in the case study 5 of Castro’s weather station, with a hit percentage of 95.7%, a Pearson correlation coefficient of 0.97 and the simple average of the errors obtained from the training the neural network of 0,094298. Given this context, it was revealed that the STST Agrometeorological is a viable alternative in the treatment of meteorological variables such as temperature and relative humidity, since there were results with hit percentage greater than 95% in the treatments of fails of the weather series studied.