RT Journal Article
SR Electronic
T1 Expanding a Hurricane Wind Resistance Rating System for Tree Species Using Machine Learning
JF Arboriculture & Urban Forestry
FD International Society of Arboriculture
SP 128
OP 153
DO 10.48044/jauf.2025.002
VO 51
IS 2
A1 Salisbury, Allyson B.
A1 Koeser, Andrew K.
A1 Andreu, Michael G.
A1 Chen, Yujuan
A1 Freeman, Zachary
A1 Miesbauer, Jason W.
A1 Herrera-Montes, Adriana
A1 Kua, Chai-Shian
A1 Nukina, Ryo Higashiguchi
A1 Rockwell, Cara
A1 Shibata, Shozo
A1 Thorn, Hunter
A1 Wan, Benyao
A1 Hauer, Richard J.
YR 2025
UL http://auf.isa-arbor.com/content/51/2/128.abstract
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.