The use of tree crown variables in over-bark diameter and volume prediction models
IForest, cilt.7, sa.3, ss.132-139, 2014 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 7 Sayı: 3
- Basım Tarihi: 2014
- Doi Numarası: 10.3832/ifor0878-007
- Dergi Adı: IForest
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.132-139
- Anahtar Kelimeler: Back-propagation ANNs, Crown variables, Generalized regression neural networks, Levenberg-marquardt ANNs, Taper
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Isparta Uygulamalı Bilimler Üniversitesi Adresli: Hayır
Özet
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. © SISEF.