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Use of machine learning techniques to model wind damage to forests

Hart, Emma; Sim, Kevin; Kamimura, Kana; Meredieu, C�line; Guyon, Dominique; Gardiner, Barry


Kana Kamimura

C�line Meredieu

Dominique Guyon

Barry Gardiner


This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory data damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle Aquitaine, France. The models were
tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested.
All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but
none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm. However, none of the models developed with the smaller forest data set could successfully
discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results.
Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available.
In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment at the stand or single tree level over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage.

Journal Article Type Article
Acceptance Date Oct 29, 2018
Online Publication Date Nov 14, 2018
Publication Date 2019-02
Deposit Date Nov 6, 2018
Publicly Available Date Nov 14, 2018
Journal Agricultural and Forest Meteorology
Print ISSN 0168-1923
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 265
Pages 16-29
Keywords Machine learning; forest damage; wind risk, risk models, GALES, forest planning
Public URL
Contract Date Nov 6, 2018


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