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https://doi.org/10.31298/sl.143.9-10.3

A comparison of artificial neural network models and regression models to predict tree volumes for crimean black pine trees in Cankiri forests

Muammer Şenyurt orcid id orcid.org/0000-0002-8957-9295 ; Çankiri Karatekin University, Forestry Faculty, Çankiri, Turkey
Ilker Ercanli orcid id orcid.org/0000-0003-4250-7371 ; Çankiri Karatekin University, Forestry Faculty, Çankiri, Turkey


Puni tekst: engleski pdf 539 Kb

str. 413-423

preuzimanja: 452

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Sažetak

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).

Ključne riječi

Tree Volume Prediction, Artificial Neural Network, Single and double volume equations, Segmented taper equation

Hrčak ID:

227251

URI

https://hrcak.srce.hr/227251

Podaci na drugim jezicima: hrvatski

Posjeta: 824 *