Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques

A machine learning model, specifically the multi-layer perceptron (MLP) neural network, has demonstrated high accuracy—97.7% across all datasets—in predicting tree failure under windstorm conditions in harvested Hyrcanian forests. This model outperformed radial basis function neural networks and support vector machines, identifying key factors such as tree height, crown diameter, and target tree height as critical determinants of susceptibility to wind-induced damage. The findings underscore the potential for integrating artificial intelligence into forest management strategies to mitigate tree failure risks during timber harvesting operations, thereby optimizing wood extraction while reducing unscheduled clear-cutting costs.

Jahani, A., & Saffariha, M. (2021). Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-020-80426-7