Document Type
Article
Publication Date
2014
College/Unit
Davis College of Agriculture, Natural Resources and Design
Department/Program/Center
Division of Forestry and Natural Resources
Abstract
Linear and nonlinear crown variable functions for 173 Brutian pine (Pinus brutia Ten.) trees were incorporated into a well-known compatible volume and taper equation to evaluate their effect in model prediction accuracy. In addition, the same crown variables were also incorporated into three neural network (NN) types (Back-Propagation, Levenberg-Marquardt and Generalized Regression Neural Networks) to investigate their applicability in over-bark diameter and stem volume predictions. The inclusion of crown ratio and crown ratio with crown length variables resulted in a significant reduction of model sum of squared error, for all models. The incorporation of the crown variables to these models significantly improved model performance. According to results, non-linear regression models were less accurate than the three types of neural network models tested for both over-bark diameter and stem volume predictions in terms of standard error of the estimate and fit index. Specifically, the generated Levenberg-Marquardt Neural Network models outperformed the other models in terms of prediction accuracy. Therefore, this type of neural network model is worth consideration in over-bark diameter and volume prediction modeling, which are some of the most challenging tasks in forest resources management.
Digital Commons Citation
Ozcelik, Ramazan; Diamantopoulou, Maria J.; and Brooks, John R., "* The use of tree crown variables in over-bark diameter and volume prediction models" (2014). Faculty & Staff Scholarship. 2597.
https://researchrepository.wvu.edu/faculty_publications/2597
Source Citation
Özçelik, R., Diamantopoulou, M., & Brooks, J. (2014). The use of tree crown variables in over-bark diameter and volume prediction models. iForest - Biogeosciences and Forestry, 7(3), 132–139. https://doi.org/10.3832/ifor0878-007
Comments
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