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.
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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
© SISEF - The Italian Society of Silviculture and Forest Ecology 2023
Open Access
This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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