evaluation of tree basal area increment models using machine learning algorithms

Forest management planning is a critical decision-making tool in forestry, resulting in a plan that outlines anticipated activities, their timing, and control measures to achieve forest management goals. investigating growth and product models is one of the most important methods for obtaining information about the future state of a forest. in other words, assessing stand growth and yield is a basic prerequisite for forest management planning. therefore, determining and estimating the basal area increment of trees is crucial for understanding forest dynamics and informing planning and management efforts. since  hyrcanian forest species are considered among the most valuable, this study aims to investigate basal area increment using machine learning algorithms and model itin the uneven-aged forest of farim in mazandaran province. in this study, the basal area increment (bai) of trees was modeled using machine learning (ml) algorithms (artificial neural networks, support vector machine, random forest, and generalized additive model) over 10 years. biometric indices (e.g., diameter, height, basal area, basal area of the largest trees), physiographic factors (aspect, slope, altitude), and climatic variables (temperature, precipitation, evaporation and transpiration) were used as input for model development. the performance  of the machine learning algorithms were compared using bias, rmse, and r2. the ann model, specifically an mlp network with seven hidden layer neurons, achieved the highest accuracy (88%) in predicting basal area increment compared to other models. these results demonstrate the effectiveness of ann models for accurately modeling basal area increment, making them valuable tools in forest management. the strong performance of the generated models, attributed to their optimal structure (e.g., number of neurons, activation function, and input variables), highlights their stability and generalization capacity  across diverse datasets. the potential to improve forest parameter modeling using machine learning techniques, specifically ann, is crucial for sustainable forest management. such improvements can enhance the conservation of species composition and the structural characteristics of the forest.

evaluation of tree basal area increment models using machine learning algorithms

forest management planning is a critical decision-making tool in forestry, resulting in a plan that outlines anticipated activities, their timing, and control measures to achieve forest management goals. investigating growth and product models is one of the most important methods for obtaining information about the future state of a forest. in other words, assessing stand growth and yield is a basic prerequisite for forest management planning. therefore, determining and estimating the basal area increment of trees is crucial for understanding forest dynamics and informing planning and management efforts. since  hyrcanian forest species are considered among the most valuable, this study aims to investigate basal area increment using machine learning algorithms and model itin the uneven-aged forest of farim in mazandaran province. in this study, the basal area increment (bai) of trees was modeled using machine learning (ml) algorithms (artificial neural networks, support vector machine, random forest, and generalized additive model) over 10 years. biometric indices (e.g., diameter, height, basal area, basal area of the largest trees), physiographic factors (aspect, slope, altitude), and climatic variables (temperature, precipitation, evaporation and transpiration) were used as input for model development. the performance  of the machine learning algorithms were compared using bias, rmse, and r2. the ann model, specifically an mlp network with seven hidden layer neurons, achieved the highest accuracy (88%) in predicting basal area increment compared to other models. these results demonstrate the effectiveness of ann models for accurately modeling basal area increment, making them valuable tools in forest management. the strong performance of the generated models, attributed to their optimal structure (e.g., number of neurons, activation function, and input variables), highlights their stability and generalization capacity  across diverse datasets. the potential to improve forest parameter modeling using machine learning techniques, specifically ann, is crucial for sustainable forest management. such improvements can enhance the conservation of species composition and the structural characteristics of the forest.