STEM TAPER ESTIMATIONS WITH ARTIFICIAL NEURAL NETWORKS FOR MIXED ORIENTAL BEECH AND KAZDAĞI FIR STANDS IN KARABÜK REGION, TURKEY

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Oytun Emre Sakici http://orcid.org/0000-0003-4961-2991 Gulay Ozdemir http://orcid.org/0000-0003-2765-084X

Abstract

Development of artificial neural network (ANN) models to estimate stem tapers of individual trees in mixed Oriental beech and Kazdağı fir stands distributed in Karabük region of Turkey, and comparison of the ANN models with stem taper equations were aimed in this study. The measurements were obtained from 516 sample trees (238 for Oriental beech and 278 for Kazdağı fir) in mixed stands of Karabük region. The measurements included diameter at breast height, tree height, diameter at stump height, and diameters at intervals of 1 m along the stem. In total, 45 ANN model structures with combinations of transfer functions used in hidden and output layers and neuron numbers in hidden layer and four stem taper equations were developed. The comparison of estimation performances of ANN models and stem taper equations were conducted using relative rankings according to seven goodness-of-fit criteria. As a result of the comparisons, the ANN models were more successful in estimation of stem taper for both tree species. The most successful ANN model structures were (i) the model using logistic function in hidden layer with 10 neurons and hyperbolic tangent function in output layer for Oriental beech, and (ii) the model using logistic function in hidden layer with 10 neurons and linear transfer in output layer for Kazdağı fir. The results reported in this study suggest that the selected ANN models are reliable for estimating the stem diameter of both tree species in mixed stands because of their unbiased results and superiority.

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How to Cite
SAKICI, Oytun Emre; OZDEMIR, Gulay. STEM TAPER ESTIMATIONS WITH ARTIFICIAL NEURAL NETWORKS FOR MIXED ORIENTAL BEECH AND KAZDAĞI FIR STANDS IN KARABÜK REGION, TURKEY. CERNE, [S.l.], v. 24, n. 4, p. 439-451, feb. 2019. ISSN 2317-6342. Available at: <http://cerne.ufla.br/site/index.php/CERNE/article/view/1933>. Date accessed: 27 mar. 2019.
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