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ŠUMARSKI LIST 9-10/2019 str. 31     <-- 31 -->        PDF

A comparison of artificial neural network models and regression models to predict tree volumes for crimean black pine trees in Cankiri forests
Usporedba modela umjetne neuralne mreže za predviđanje drvnog volumena krimskih borova u šumama pokrajine Cankiri
Muammer Şenyurt, Ilker Ercanli
In this study, it is aimed to use and compare Artificial Neural Network (ANN) models for predicting individual tree volumes for of Crimean Black Pine trees within the Cankiri Forests. The single and double entry-volume equations and the Fang et al. (2000)’s compatible volume equation based on the classical and traditional methods were used by 360 Crimean Black Pine trees to obtain these tree volume predictions. To determine the best predictive alternative for ANN models, a total of 320 trained networks in the Multilayer Perceptron (MLP) and a total of 20 trained networks in the Radial Basis Function (RBF) architectures was trained and used to obtain the individual tree volume predictions. On the basis of the goodness-of-fit statistics, the ANN-based on MLP 1-9-1 including dbh as an input variable for single entry volume predictions showed a better fitting ability with SSE (2.7763), (0.9339), MSE (0.00910), RMSE (0.0954), AIC (-823.25) and SBC (-1421.81) than that by the other studied volume methods including dbh as an explanatory variable. For double entry volume predictions, including dbh and total height as input variables, ANN based on MLP 2-15-1 resulted in better fitting statistics with SSE (0.8354), (0.9801), MSE (0.00274), RMSE (0.0523), AIC (–579.55) and SBC (–1788.11).
Key words: Tree Volume Prediction, Artificial Neural Network, Single and double volume equations, Segmented taper equation
The individual tree volume predictions have been a principal objective of forest inventory studies and these predictions require both the sustainable planning of forest resources and for the estimations of the forest biomass and carbon stocks (Wiant et. al., 1992; Avery and Burkhart, 2002). Since foresters have a wide part of being aware of the primary importance of volume predictions for forest practices such as sustainable resource management and timber harvesting operations, forest managers have spent many efforts for