Aplicação de redes neurais artificiais para a classificação da capacidade produtiva de um povoamento florestal na região do Jari, Oeste do Pará - GAMA, Natally Celestino (2021)
The objective of this work was to evaluate the classification of productive capacity using traditional methods and compare with the classification obtained through the use of Artificial Neural Networks in Eucalyptus urograndis plantations in the Amazon. Data are from non-thinned clonal plantations of Eucalyptus urograndis hybrids located in the Jari region of western Pará state. For the classification of the productive capacity the following methods were evaluated: guide curve (MCG), difference equation (MED) and parameter prediction (MPP). The statistical criteria used to assess the quality of the classification were: BIAS (%), square root mean error (RQEM (%)), correlation coefficient (rŷ.y) and the Bayesian Information criterion (BIC). The classification through artificiais neurais networks was superior to that obtained by traditional methods, which demonstrates the potential of this tool in the classification of productive capacity of forest stands.