AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK

Abstract

This study presents a diametric distribution model based on a one-dimensional cellular automata model (CA) and artificial neural network (ANN). Each cell of CA represents a dbh class, with the future state predicted in function of the present state of this cell, of the four neighboring cells and of its present and future age. An ANN was used as rule of evolution. Accuracy was evaluated by applying: statistical procedure proposed by Leite and Oliveira (2002); relation between observed and estimated frequency; and biological realism of the built model. Of the trained networks, were selected the ten representing the evolution of the diameter distribution with greater accuracy. Among these ten ANN, seven had estimated values statistically equal to observed (p>0.01). The proposed modeling approach estimates accurately future diameter distributions.