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Economic and environmental consequences of harvesting operations are important in forestry because of the damages caused by tree felling and timber transportation. In this research, we analyzed forest stand damage susceptibility using GIS-based logistic regression (LR) and boosted regression tree (BRT) machine learning models for the Shourab Forests. These forests contain valuable stands of Hyrcanian forests in northern Iran that represent many of the plant covers, geographical, and forest management features of this ancient ecosystem. To this end, we applied traversed transect to cover the forest unit. This allowed us to sample any damaged residual tree intersected by the transect resulting in a dataset of 152 damaged trees in the study area. Of this dataset, we utilized 106 damaged trees for training purposes, while the remaining instances (46 damage locations) served as the verification dataset. We …