| dc.creator |
Ozcelik, RAMAZAN |
|
| dc.creator |
Diamantopoulou, Maria J. |
|
| dc.creator |
CRECENTE-CAMPO, Felipe |
|
| dc.creator |
ELER, Unal |
|
| dc.date |
2013-10-14T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T09:49:45Z |
|
| dc.date.available |
2020-10-06T09:49:45Z |
|
| dc.identifier |
4a123cc1-9408-4e09-9014-a1ec42dff356 |
|
| dc.identifier |
10.1016/j.foreco.2013.06.009 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/4a123cc1-9408-4e09-9014-a1ec42dff356/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/59290 |
|
| dc.description |
Artificial neural network models offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which are very helpful in tree height modeling. Back-propagation artificial neural network models were produced for individual-tree height estimation and the results were compared with the most used tree height estimation methods. Height diameter (h-d) measurements of 1163 Crimean juniper trees in 63 sample plots from southwestern region of Turkey were used. A calibrated basic h-d mixed model, a generalized h-d model and back-propagation artificial neural network h-d models were constructed and compared. When the variability of the h-d relationship fronl. ss stand can be incorporated into the model, then both mixed-effects nonlinear regression and back propagation neural network modeling approaches can produce accurate results, reducing the root mean squared error by more than 20% as compared to a basic nonlinear regression model. The use of a generalized h-d model also showed reliable results (reduction of 13% in root mean squared error as compared to a nonlinear regression model). The back-propagation artificial neural network model seems a reliable alternative to the other methods examined possessing the best generalization ability. Further, from a practical point of view it has the advantage that no height measurements are needed for its implementation. On the contrary prior information is required for the mixed-effects model calibration which is a limiting factor according to its use. (C) 2013 Elsevier B.V. All rights reserved. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models |
|
| dc.type |
info:eu-repo/semantics/article |
|