Improving the Efficiency of Allometric Equations using Artificial Neural Networks in Coppicing Stands of Brant's Oak

Abstract

Abstract Estimation of forest trees biomass for various purposes is fundamental. One method of estimating biomass uses allometric equations that limit the normality of variables and the homogeneity of variances. In this study, artificial neural networks were used as an alternative method to increase biomass estimation accuracy. Fifty three sprout chumps of Brant's Oak (Quercus brantii Lindl) were randomly selected from the Melah¬Shabanan of Khorramabad in Iran. Diameter at knee height, diameter at breast height, crown diameter, number of sprouts, and height of trees were measured. To calculate the dry weight of the biomass, a disk 3–5 cm from the trunk and crown was separated and weighed, and with the ratio of dry weight to fresh weight, the dry weight of the crown, trunk, and aboveground biomass of the trees was calculated. Modeling the relationships between variables with regression equations and Multilayer Perceptron and Radial Basis Function neural networks showed that both neural networks could increase the coefficient of determination to R2 = 0.98 and R2 = 0.96 and reduce the error to RMSE% = 11.6 and RMSE% = 16.9 and thus the neural network models can increase the quality forest biomass estimates are compared with allometric equations.