Expanding a Hurricane Wind Resistance Rating System for Tree Species Using Machine Learning
Article
Salisbury, AB, Koeser, AK, Andreu, MG et al. (2025). Expanding a Hurricane Wind Resistance Rating System for Tree Species Using Machine Learning
. 51(2), 128-153. 10.48044/jauf.2025.002
Salisbury, AB, Koeser, AK, Andreu, MG et al. (2025). Expanding a Hurricane Wind Resistance Rating System for Tree Species Using Machine Learning
. 51(2), 128-153. 10.48044/jauf.2025.002
Background: Hurricanes and other wind events are significant disturbances that affect coastal urban forests around the world. Past research has led to the creation of wind resistance ratings for different tree species, which can be used in urban forest management efforts to mitigate the effects of these storms. While useful, these ratings have been limited to species common to urban forestry in Florida, USA. Methods: Drawing on past ratings and data from a global literature review on tropical storm research, we created a machine learning model to broaden both the geographic coverage and the variety of species currently assessed for their resistance to wind. Results: We assigned wind resistance ratings to 281 new species based on the available data and our modelling efforts. The model accuracy and agreement with the original ratings when applied to the testing data set was high with 91% accuracy. Conclusions: Our study demonstrated how a machine learning algorithm can be used to expand rating systems to include new species given sufficient data. Communities can use the expanded wind resistance rating species list to choose wind resistant species for planting and focus risk assessment on low wind resistant trees.
