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

Details

Ressource 1Download: geosciences-12-00424.pdf (5628.54 [Ko])
State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_2A98F3B50263
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes' Importance Ranking in Wildfire Susceptibility
Journal
Geosciences
Author(s)
Trucchia Andrea, Izadgoshasb Hamed, Isnardi Sara, Fiorucci Paolo, Tonini Marj
ISSN
2076-3263
Publication state
Published
Issued date
18/11/2022
Peer-reviewed
Oui
Volume
12
Number
11
Pages
424
Language
english
Abstract
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.
Keywords
random forest, multi-layer perceptron, support vector machine, vegetation types, partial dependent plot, variable importance ranking, Liguria
Open Access
Yes
Create date
23/11/2022 16:45
Last modification date
11/01/2023 7:52
Usage data