PT  - JOURNAL ARTICLE
AU  - Salisbury, Allyson B.
AU  - Koeser, Andrew K.
AU  - Andreu, Michael G.
AU  - Chen, Yujuan
AU  - Freeman, Zachary
AU  - Miesbauer, Jason W.
AU  - Herrera-Montes, Adriana
AU  - Kua, Chai-Shian
AU  - Nukina, Ryo Higashiguchi
AU  - Rockwell, Cara
AU  - Shibata, Shozo
AU  - Thorn, Hunter
AU  - Wan, Benyao
AU  - Hauer, Richard J.
TI  - Expanding a Hurricane Wind Resistance Rating System for Tree Species Using Machine Learning
AID  - 10.48044/jauf.2025.002
DP  - 2025 Mar 01
TA  - Arboriculture & Urban Forestry
PG  - 128--153
VI  - 51
IP  - 2
4099  - http://auf.isa-arbor.com/content/51/2/128.short
4100  - http://auf.isa-arbor.com/content/51/2/128.full
AB  - 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.