Tree stem mean diameter reduction factor prediction through advanced modeling approaches

Maria Diamantopoulou

Authors

Maria Diamantopoulou School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Greece

Abstract

Sustainable management of natural resources relies on accurate modellingof forest attributes to prevent degradation. This study explores advanced modellingtechniques, including Artificial Neural Networks (ANN) and Support VectorRegression (SVR), for estimating the mean stem diameter reduction factor (taper)of standing fir trees (Abies x borisii-regis Matff.). These methods are comparedagainst traditional non-linear regression model (NLR), developed using theLevenberg-Marquardt optimization algorithm. The ANN models employ cascadecorrelation, generalized regression, and Bayesian regularization back-propagationarchitectures, while the ε-SVR approach is assessed for its robustness. The resultsshow that support vector regression (ε-SVR) achieved the lowest relative errorsin model fitting, improving by 0.60% over cascade correlation and generalizedregression and by 0.67% over Bayesian regularization. Regarding generalizationability, the ε-SVR model performed best, with a relative error of 4.90%, whichwas slightly lower than cascade correlation (by 0.1%), generalized regression (by0.01%), and Bayesian regularization (by 0.04%). A comparative analysis betweenmachine learning approaches and standard regression revealed that the ε-SVRmodel had the lowest mean error (0.0715), while the non-linear regression (NLR)model showed a higher mean error of 0.0955, which means 1.35 times greater.These findings highlight the strong capability of machine learning methodsin accurately estimating and predicting the diameter reduction factor of trees,effectively capturing its non-linear behaviour compared to traditional regressionmodels. Overall, this study underscores the potential of advanced machine learningtechniques to enhance accuracy and adaptability in sustainable forest management.

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Tree stem mean diameter reduction factor prediction through advanced modeling approaches

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