Expanding a Hurricane Wind Resistance Rating System for Tree Species

How to cite: Salisbury, A.B.; Koeser, A.K.; Andreu, M.G.; Chen, Y.; Freeman, Z.; Miesbauer, J.W.; Herrera-Montes, A.; Kua, C.; Nukina, R.H.; Rockwell, C.; Shibata, S.; Thorn, H.; Wan, B.; Hauer, R.J. Expanding a Hurricane Wind Resistance Rating System for Tree Species. Preprints 2024, 2024041495. https://doi.org/10.20944/preprints202404.1495.v1 Salisbury, A.B.; Koeser, A.K.; Andreu, M.G.; Chen, Y.; Freeman, Z.; Miesbauer, J.W.; Herrera-Montes, A.; Kua, C.; Nukina, R.H.; Rockwell, C.; Shibata, S.; Thorn, H.; Wan, B.; Hauer, R.J. Expanding a Hurricane Wind Resistance Rating System for Tree Species. Preprints 2024, 2024041495. https://doi.org/10.20944/preprints202404.1495.v1

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MDPI and ACS Style

Salisbury, A.B.; Koeser, A.K.; Andreu, M.G.; Chen, Y.; Freeman, Z.; Miesbauer, J.W.; Herrera-Montes, A.; Kua, C.; Nukina, R.H.; Rockwell, C.; Shibata, S.; Thorn, H.; Wan, B.; Hauer, R.J. Expanding a Hurricane Wind Resistance Rating System for Tree Species. Preprints 2024, 2024041495. https://doi.org/10.20944/preprints202404.1495.v1

APA Style

Salisbury, A.B., Koeser, A.K., Andreu, M.G., Chen, Y., Freeman, Z., Miesbauer, J.W., Herrera-Montes, A., Kua, C., Nukina, R.H., Rockwell, C., Shibata, S., Thorn, H., Wan, B., & Hauer, R.J. (2024). Expanding a Hurricane Wind Resistance Rating System for Tree Species. Preprints. https://doi.org/10.20944/preprints202404.1495.v1

Chicago/Turabian Style

Salisbury, A.B., Benyao Wan and Richard J. Hauer. 2024 "Expanding a Hurricane Wind Resistance Rating System for Tree Species" Preprints. https://doi.org/10.20944/preprints202404.1495.v1

Abstract

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 one global region (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 were able to assign 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: The resulting list of wind resistance ratings has been adapted into a spreadsheet-based decision aid for managers, allowing them to assess the overall susceptibility of their urban forest to wind events like hurricanes.

Keywords

Cyclone; Risk Management; Species Selection; Tree Failure; Typhoon

Subject

Biology and Life Sciences, Forestry

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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