BIOMARCADORES NO DIAGNÓSTICO PRECOCE DA INJÚRIA RENAL AGUDA

Abstract

The aim of this study was to evaluate the efficiency of using an artificial neural network (ANN) to estimate the stem taper of eucalypt trees in a silvopastoral system composed with two spatial arrangements. The data used were collected out of 35 sample-trees scaled in a silvipastoral system with spatial arrangements of 12 m x 4 m and 12 m x 2 m. The taper model proposed by Garay was fitted for each spatial arrangement. Also, the ANN with Multilayer Perceptron configuration, using the spatial arrangement as a categorical variable, was trained. The others input variables for the ANN were the diameter at breast height - 1.30 m height, total height, height of each section and the corresponding diameters. The accuracy of the methods was evaluated using the Root Mean-square Error, the correlation between observed and estimated diameters, dispersion of percentage errors and the mean absolut deviaton. The ANN achieved a similar performance compared to the two functions of tapering, proving to be an appropriate methodology for small eucalyptus plantations in silvopastoral system, where there may be restrictions for logging.