| dc.creator |
Diamantopoulou, Maria J. |
|
| dc.creator |
Brooks, John R. |
|
| dc.creator |
Ozcelik, RAMAZAN |
|
| dc.date |
2014-01-12T22:00:00Z |
|
| dc.date.accessioned |
2020-10-06T09:27:32Z |
|
| dc.date.available |
2020-10-06T09:27:32Z |
|
| dc.identifier |
19ac16dd-633c-4fe4-a480-345b08c6e8bd |
|
| dc.identifier |
10.3832/ifor0878-007 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/19ac16dd-633c-4fe4-a480-345b08c6e8bd/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/54429 |
|
| dc.description |
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. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
The use of tree crown variables in over-bark diameter and volume prediction models |
|
| dc.type |
info:eu-repo/semantics/article |
|