Aplicação de redes neurais artificiais na estimativa de altura de um plantio clonal de Eucalyptus urophylla S. T. Blake em Altamira, PA - MORAES, Rylla Bryanne (2019)
The production prognosis is performed through growth and production modeling, where volume estimates are obtained as a function of variables such as DBH and height, for example. The process of collecting height data in the field demands greater time and cost, and hypsometric equations are often used and in recent years artificial neural networks to obtain height estimates. Thus, the objective of this work was to evaluate the application of RNA in the height estimation of a clonal Eucalyptus urophylla S. T. Blake planting in Almeirim, Pará, comparing it with the estimates obtained by the hypsometric equation. For the training of the networks and adjustment of the equations, data from 540 eucalyptus individuals, aged 24 to 72 months, were used annually between 2013 and 2017. The Gompertz model was used, and the multilayer percepton network, and the estimates obtained were evaluated by means of the correlation coefficient between observed and estimated heights, square error of the mean error (RQEM), Bias, and graphical analysis of the waste distribution. The two procedures evaluated were efficient for estimating the height of Eucalyptus urophylla S. T. Blake trees, however, the RNA was higher in the statistical indicators RQEM and Bias in relation to the regression.