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

Abstract

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.

Analyzing regression models and multi-layer artificial neural network models for estimating taper and tree volume in Crimean pine forests

“…A unique feature of this study is the special focus on eliminating overfitting problems and violations of biological realism in SPTE and TTV. Overfitting is a common issue in forestry when estimating various individual tree and stand properties (Ercanli et al 2022, Bolat et al 2023) which can cause ANN models to be very effective for their own training datasets but very unsuccessful when tested on independent datasets. ANN modeling in forestry must take into account the biological realism of the model predictions, as extensively discussed in the literature (Avery & Burkhart 1983, Van Laar & Akça 2007, Pretzsch 2009.…”

Exploring machine learning modeling approaches for biomass and carbon dioxide weight estimation in Lebanon cedar trees

“…Successful effort to produce reliable and accurate models for tree biomass estimation using ML techniques have been previously reported (Guo et al 2012, Ozçelik et al 2017, Malek et al 2019, Güner et al 2022. In addition, Artificial Neural Networks (ANNs) and Support Vector Machines for regression (SVR), have recently gained scientific interest in forestry research (Youquan et al 2012, Ozçelik et al 2013, Binoti et al 2016, Tavares Júnior et al 2019, Bolat et al 2023, thanks to their independence of a priori specifications of the (i) form of an equation describing the ground truth data, (ii) data distribution, and (iii) potential transformations of the variables, which are all to be matched in the case of regression modeling. In particular, ANN modeling is considered as a valid alternative to non-linear modeling, especially for complex biological ecosystems such as the forests (Wu 2014, Özçelik et al 2017, Malek et al 2019, Güner et al 2022.…”