2017-08-212017-08-212014-05-09FREIRE, Jean Carlos Arouche. Aplicação de técnicas estatísticas e de inteligência computacional na classificação de ciclos hidrológicos em reservatórios de água na região amazônica: um estudo de caso. 2014. 67 f. Dissertação (Mestrado) - Universidade Federal do Pará, Instituto de Ciências Exatas e Naturais, Belém, 2014. Programa de Pós-Graduação em Ciência da Computação.https://repositorio.ufpa.br/handle/2011/9010This study evaluates the quality of the water reservoir of the Hydroelectric Plant Tucuruí according to the regional hydrological cycle and the spatial arrangement of the different sampling sites distributed in areas upstream of the dam in the period 2009-2012 from the amendment of 17 parameters physico-chemical and metals from water extracted of six factors that accounted for 71.01% of total data variability. It was observed that the greatest variations of NO3, NH4, , totalP, PO4 and STS occurred in the period of floods and may be an indication of trophic status in the sampling sites due to the existence of fishing poles or population density in the vicinity these sites. Discriminant analysis, artificial neural networks, k-nearest neighbors, support vector machine with polynomial and radial core and random forest: classification of the hydrological cycle to six classifiers were used. The results indicate that the random forest classifier showed the best performance with a percentage rating of 7.80% of incorrect predictions. While Student t test indicates that random forest and k-nearest neighbors have an average rate of incorrect predictions with equal significance index set at α = 5%.Acesso AbertoInteligência computacionalHidrologiaClassificação de ciclos hidrológicosEstatística matemática - Processamento de dadosReservatóriosUsina Hidrelétrica de Tucuruí - PATucuruí - PAPará - EstadoDissertaçãoCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO