Assessment of a probabilistic supervised machine learning method to estimate biomass expansion and conversion factors: A case study on cedar and pine trees.

Quantifying tree and forest biomass is crucial for formulating effective forest policy and management, given its role in human resource use and carbon storage. Forest biomass significantly contributes to environmental quality by absorbing carbon dioxide. Current research focuses on determining biomass factors for various tree species. This study employed both standard non-linear regression (NLR) and Gaussian process regression (GPR), a machine learning method using artificial intelligence, to estimate and predict biomass expansion and conversion factors accurately. The case study included plantation and naturally occurring cedar and pine trees in Türkiye's Western Anatolian Region and Göller Region. Non-linear regression used Levenberg-Marquardt optimization method, while Gaussian process regression employed radial basis function kernel. This dual approach allowed for assessing prediction uncertainties. The models constructed using GPR show superior performance compared to NLR models for both biomass factors and species within the datasets used. According to Furnival evaluation metric values, accuracy of the NLR models was 1.05 to 1.34 times lower than that of corresponding GPR models. Overall findings highlight the significant potential of Gaussian process regression for accurately estimating and predicting biomass factors with high variances. This emphasizes its utility in modeling scenarios that require high flexibility, such as tree biomass prediction.