Reconhecimento do Padrão Pluvial na cidade de Presidente Prudente - SP através de rede neural artificial

Abstract

The Artificial Neural Networks are nonlinear mathematical models that resemble the human brain, and this ability to learn was applied to recognize the rain patterns in the city of Presidente Prudente, located in the region of Pontal do Paranapanema. Through these calculations, it was possible to indicate another way to rain forecast. This study used two algorithms with supervised learning, the first one the Multiple Layer Network Propagation, with 23 neurons and with one, two and three hidden layers, and the second one the Support Vector Machine (SVM) with polynomial, radial basis function and hyper tangent cores. The set analyzed covers the period from January 1996 to May 2012, collected from Weather Forecast Center and Climate Studies (CPTEC). The results showed that the atmospheric pressure, wind direction, minimum temperature and air relative humidity were the parameters more related with the rain precipitation. The SVM model with base radial function core, using sigma=0.1, showed the best results with Kappa coefficient, equal to 0.675 for first test group, equal to 0.746 to the second test group 0.746 and equal to 0.826 for the third test group. These results demonstrate the data set robustness and allowed achieve high accuracy rate in recognition of rain precipitation.