A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_FAC31D6A6D4B
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy
Journal
Geosciences
Author(s)
Tonini Marj, D'Andrea Mirko, Biondi Guido, Degli Esposti Silvia, Trucchia Andrea, Fiorucci Paolo
ISSN
2076-3263
Publication state
Published
Issued date
18/03/2020
Peer-reviewed
Oui
Volume
10
Number
3
Pages
105
Language
english
Abstract
Wildfire susceptibility maps display the spatial probability of an area to burn in the future, based solely on the intrinsic local proprieties of a site. Current studies in this field often rely on statistical models, often improved by expert knowledge for data retrieving and processing. In the last few years, machine learning algorithms have proven to be successful in this domain, thanks to their capability of learning from data through the modeling of hidden relationships. In the present study, authors introduce an approach based on random forests, allowing elaborating a wildfire susceptibility map for the Liguria region in Italy. This region is highly affected by wildfires due to the dense and heterogeneous vegetation, with more than 70% of its surface covered by forests, and due to the favorable climatic conditions. Susceptibility was assessed by considering the dataset of the mapped fire perimeters, spanning a 21-year period (1997–2017) and different geo-environmental predisposing factors (i.e., land cover, vegetation type, road network, altitude, and derivatives). One main objective was to compare different models in order to evaluate the effect of: (i) including or excluding the neighboring vegetation type as additional predisposing factors and (ii) using an increasing number of folds in the spatial-cross validation procedure. Susceptibility maps for the two fire seasons were finally elaborated and validated. Results highlighted the capacity of the proposed approach to identify areas that could be affected by wildfires in the near future, as well as its goodness in assessing the efficiency of fire-fighting activities.
Keywords
wildfires, susceptibility mapping, machine learning, random forest, spatial-cross validation
Open Access
Yes
Funding(s)
Swiss National Science Foundation / IZSEZ0_186483
Create date
26/03/2020 9:42
Last modification date
11/01/2023 6:52
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