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iForest - Biogeosciences and Forestry

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Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors

Ferhat Bolat   , Ilker Ercanli, Alkan Günlü

iForest - Biogeosciences and Forestry, Volume 16, Issue 1, Pages 30-37 (2023)
doi: https://doi.org/10.3832/ifor4116-015
Published: Jan 22, 2023 - Copyright © 2023 SISEF

Research Articles


Models of forest growth and yield provide important information on stand and tree developments and the interactions of these developments with silvicultural treatments. These models have been developed based on assumptions such as independence of observations, uncorrelated error terms, and error terms with constant variance; if these factors are absent, there may be problems with multicollinearity, autocorrelation, or heteroscedasticity, respectively. These problems, which have several adverse effects on parameter estimates, are statistical phenomena and must be avoided. In recent years, the artificial neural network (ANN) model, thanks to its superior features such as the ability to make successful predictions and the absence of the requirement for statistical assumptions, has been commonly used in forestry modeling. However, while goodness-of-fit measures were taken into consideration in the assessment of ANN models, the control of the biological characteristics of model predictions was ignored. In this study, variable-density yield models were developed using nonlinear regression and ANN techniques. These modeling techniques were compared based on some goodness-of-fit measures and the principles of forest yield. The results showed that ANN models were more successful in meeting expected biological patterns than regression models.

  Keywords


Bayesian, Machine Learning, Gompertz, Overfitting

Authors’ address

(1)
Ferhat Bolat 0000-0003-2655-5023
Ilker Ercanli 0000-0003-4250-7371
Alkan Günlü 0000-0002-6458-6165
Çankiri Karatekin University, Faculty of Forestry, 18200, Çankiri (Turkey)

Corresponding author

 

Citation

Bolat F, Ercanli I, Günlü A (2023). Yield of forests in Ankara Regional Directory of Forestry in Turkey: comparison of regression and artificial neural network models based on statistical and biological behaviors. iForest 16: 30-37. - doi: 10.3832/ifor4116-015

Academic Editor

Maurizio Marchi

Paper history

Received: Apr 14, 2022
Accepted: Nov 04, 2022

First online: Jan 22, 2023
Publication Date: Feb 28, 2023
Publication Time: 2.63 months

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