Afficher la notice abrégée

Use of machine learning techniques to model wind damage to forests

hal.structure.identifierEdinburgh Napier University
dc.contributor.authorHART, Emma
hal.structure.identifierEdinburgh Napier University
dc.contributor.authorSIM, Kevin
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
hal.structure.identifierInstitute of Mountain Science
dc.contributor.authorKAMIMURA, Kana
hal.structure.identifierUnité Expérimentale Forêt Pierroton [UEFP]
dc.contributor.authorMEREDIEU, Céline
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorGUYON, Dominique
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
hal.structure.identifierEFI Atlantic
dc.contributor.authorGARDINER, Barry
dc.date.accessioned2024-04-08T12:05:01Z
dc.date.available2024-04-08T12:05:01Z
dc.date.issued2019
dc.identifier.issn0168-1923
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/196336
dc.description.abstractEnThis 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 plots 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 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.
dc.language.isoen
dc.publisherElsevier Masson
dc.subjectforest damage
dc.subjectGALES
dc.subject.enmachine learning
dc.subject.enwind risk
dc.subject.enrisk models
dc.subject.enforest planning
dc.title.enUse of machine learning techniques to model wind damage to forests
dc.typeArticle de revue
dc.identifier.doi10.1016/j.agrformet.2018.10.022
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halSciences de l'environnement
bordeaux.journalAgricultural and Forest Meteorology
bordeaux.page16-29
bordeaux.volume265
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-02624477
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02624477v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Agricultural%20and%20Forest%20Meteorology&rft.date=2019&rft.volume=265&rft.spage=16-29&rft.epage=16-29&rft.eissn=0168-1923&rft.issn=0168-1923&rft.au=HART,%20Emma&SIM,%20Kevin&KAMIMURA,%20Kana&MEREDIEU,%20C%C3%A9line&GUYON,%20Dominique&rft.genre=article


Fichier(s) constituant ce document

FichiersTailleFormatVue

Il n'y a pas de fichiers associés à ce document.

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée