Artificial neural networks as an alternative tool in pine bark volume estimation
The utilization of pine trees bark is receiving increasing attention. For management purposes, it is very useful to be able to estimate bark volume quantity of standing trees. This paper is concerned with the use of artificial neural network (ANN) models for accurate bark volume estimation of standing pine trees (Pinus brutia) as an alternative to regression models. The procedure that should be followed in the development of such models is outlined. Five different nonlinear regression models were fitted to data using the LevenbergMarquardt optimization algorithm. Because of the bias detected on the assumptions and the high error values, three-layer feed-forward cascade correlation ANN models were developed to estimate bark volume. A comparative study of both nonlinear regression models and selected cascade correlation ANN model with the structure of 4-7-1/0.9982 indicated that the ANN model performed better than the best nonlinear regression model with error values 6.02% (root mean square error) and 13.3% (Furnival's index) of the mean bark volume, respectively. Paired t-test, 45 line test and absolute deviation percentage were also used for validation of the chosen models. The results clearly demonstrate the superiority of the ANN models to the regression models due to their ability to overcome the problems in forest data, such as nonlinear relationships, non-Gaussian distributions, outliers and noise in the data. The ANN technique introduced in this study is sufficiently general and has great potential to be applicable to many forest modeling applications providing a very useful tool as an alternative to traditional regression models.
artificial neural networks bark cascade correlation regression analysis