Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes' Importance Ranking in Wildfire Susceptibility

Détails

Ressource 1Télécharger: geosciences-12-00424.pdf (5628.54 [Ko])
Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_2A98F3B50263
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes' Importance Ranking in Wildfire Susceptibility
Périodique
Geosciences
Auteur⸱e⸱s
Trucchia Andrea, Izadgoshasb Hamed, Isnardi Sara, Fiorucci Paolo, Tonini Marj
ISSN
2076-3263
Statut éditorial
Publié
Date de publication
18/11/2022
Peer-reviewed
Oui
Volume
12
Numéro
11
Pages
424
Langue
anglais
Résumé
Susceptibility mapping represents a modern tool to support forest protection plans and to address fuel management. With the present work, we continue with a research framework developed in a pioneristic study at the local scale for Liguria (Italy) and recently adapted to the national scale. In these previous works, a random-forest-based modeling workflow was developed to assess susceptibility to wildfires under the influence of a number of environmental predictors. The main novelties and contributions of the present study are: (i) we compared models based on random forest, multi-layer perceptron, and support vector machine, to estimate their prediction capabilities; (ii) we used a more accurate vegetation map as predictor, allowing us to evaluate the impacts of different types of local and neighboring vegetation on wildfires’ occurrence; (iii) we improved the selection of the testing dataset, in order to take into account the temporal variability of the burning seasons. Wildfire susceptibility maps were finally created based on the output probabilistic predicted values from the three machine-learning algorithms. As revealed with random forest, vegetation is so far the most important predictor variable; the marginal effect of each type of vegetation was then evaluated and discussed.
Mots-clé
random forest, multi-layer perceptron, support vector machine, vegetation types, partial dependent plot, variable importance ranking, Liguria
Open Access
Oui
Création de la notice
23/11/2022 15:45
Dernière modification de la notice
11/01/2023 6:52
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