DOI: 10.1016/j.foreco.2013.06.009
Scopus记录号: 2-s2.0-84880440064
论文题名: Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models
作者: Özçelik R. ; Diamantopoulou M.J. ; Crecente-Campo F. ; Eler U.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 306 起始页码: 52
结束页码: 60
语种: 英语
英文关键词: Back-propagation neural network model
; Generalized h-d model
; Mixed-effects model
; Tree height estimation
Scopus关键词: Artificial neural network models
; Back propagation artificial neural network (BPANN)
; Back propagation neural networks
; Mixed-effects models
; Non-linear relationships
; Nonlinear regression models
; Root mean squared errors
; Tree height estimation
; Backpropagation
; Estimation
; Forestry
; Mathematical models
; Mean square error
; Neural networks
; Regression analysis
; Nonlinear analysis
; artificial neural network
; back propagation
; coniferous tree
; height
; input-output analysis
; limiting factor
; modeling
; nonlinearity
; regression analysis
; Forestry
; Mathematical Models
; Neural Networks
; Regression Analysis
; Turkey
英文摘要: 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 from stand to 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. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66424
Appears in Collections: 影响、适应和脆弱性
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作者单位: Faculty of Forestry, Süleyman Demirel University, East Campus, 32260 Isparta, Turkey; Faculty of Forestry and Natural Environment, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece; Departamento de Ingenieri and oacute;a Agroforestal, Universidad de Santiago de Compostela, Escuela Polite and oacute;cnica Superior, R Benigno Ledo, Campus universitario, 27002 Lugo, Spain
Recommended Citation:
Özçelik R.,Diamantopoulou M.J.,Crecente-Campo F.,et al. Estimating Crimean juniper tree height using nonlinear regression and artificial neural network models[J]. Forest Ecology and Management,2013-01-01,306