Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 30 January 2020 / Approved: 31 January 2020 / Online: 31 January 2020 (11:40:30 CET)

How to cite: Tonini, M.; D'Andrea, M.; Biondi, G.; Degli Esposti, S.; Trucchia, A.; Fiorucci, P. A Machine Learning based Approach for Wildfire Susceptibility Mapping. The Case Study of Liguria Region in Italy. Preprints 2020, 2020010385 (doi: 10.20944/preprints202001.0385.v1). Tonini, M.; D'Andrea, M.; Biondi, G.; Degli Esposti, S.; Trucchia, A.; Fiorucci, P. A Machine Learning based Approach for Wildfire Susceptibility Mapping. The Case Study of Liguria Region in Italy. Preprints 2020, 2020010385 (doi: 10.20944/preprints202001.0385.v1).

Abstract

Wildfire susceptibility maps display the wildfires occurrence probability, ranked from low to high, under a given environmental context. Current studies in this field often rely on expert knowledge, including or not statistical models allowing to assess the cause-effect correlation. Machine learning (ML) algorithms can perform very well and be more generalizable thanks to their capability of learning from and make predictions on data. Italy is highly affected by wildfires due to the high heterogeneity of the territory and to the predisposing meteorological conditions. The main objective of the present study is to elaborate a wildfire susceptibility map for Liguria region (Italy) by applying Random Forest, an ensemble ML algorithm based on decision trees. Susceptibility was assessed by evaluating the probability for an area to burn in the future considering where wildfires occurred in the past and which are the geo-environmental factors that favor their spread. Different models were compared, including or not the neighboring vegetation and using an increasing number of folds for the spatial-cross validation. Susceptibility maps for the two fire seasons were finally elaborated and validated and results critically discussed highlighting the capacity of the proposed approach to identify the efficiency of fire fighting activities.

Subject Areas

wildfires; susceptibility mapping; machine learning; random forest; model validation; Liguria region

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