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

Models of forest growth and yield provide important information on stand and tree developments and the interactions of these developments with silvicul-tural 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 prob-lems with multicollinearity, autocorrelation, or heteroscedasticity, respec-tively. These problems, which have several adverse effects on parameter esti-mates, are statistical phenomena and must be avoided. In recent years, the ar-tificial 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. How-ever, while goodness-of-fit measures were taken into consideration in the as-sessment of ANN models, the control of the biological characteristics of model predictions was ignored. In this study, variable-density yield models were de-veloped using nonlinear regression and ANN techniques. These modeling tech-niques were compared based on some goodness-of-fit measures and the princi-ples of forest yield. The results showed that ANN models were more successful in meeting expected biological patterns than regression models.