Wildfire susceptibility mapping via machine learning: the case study of Liguria Region, Italy
Authors: Fiorucci Paolo , D'Andrea Mirko , Trucchia Andrea , Tonini Marj 
Published: March 2020

&lt;p&gt;Risk and susceptibility analyses for&amp;#160; natural hazards are of great importance for the sake of&amp;#160; civil protection, land use planning&amp;#160; and risk reduction programs. Susceptibility maps are based on the assumption that future events are expected to occur under similar conditions as the observed ones. Each unit area is assessed in term of relative spatial likelihood, evaluating the potential to experience a particular hazard in the future based solely on the intrinsic local characteristics. These concept is well-consolidated in the research area related with the risk assessment, especially for landslides. Nevertheless, the need exist for developing new quantitative and robust methods allowing to elaborate susceptibility&amp;#160; maps and to apply this tool to the study of other natural hazards.&amp;#160; In&amp;#160; the presented work, such&amp;#160; task is pursued for the specific&amp;#160; case of wildfires in Italy. The&amp;#160; two main approaches for such studies are the adoption&amp;#160; of physically based models and the data driven methods. In&amp;#160; the presented work, the latter&amp;#160; approach is&amp;#160; pursued, using&amp;#160; Machine Learning techniques in order to learn&amp;#160; from and make prediction&amp;#160; on the available information (i.e. the observed burned area and the predisposing factors) . Italy is severely affected by wildfires due to the high topographic and vegetation heterogeneity of its territory&amp;#160; and&amp;#160; to&amp;#160; its&amp;#160;&amp;#160; meteorological conditions. The present study has as its main objective the&amp;#160; elaboration of a wildfire susceptibility map for Liguria region (Italy) by making use of Random Forest, an ensemble ML algorithm based on decision trees. The quantitative evaluation of susceptibility is carried out considering two different aspects: the location of past&amp;#160; wildfire occurrences, in terms of burned area, and the related anthropogenic and geo-environmental&amp;#160; predisposing factors that may favor fire spread. Different implementation of the model&amp;#160; were performed and compared. In&amp;#160; particular,&amp;#160; the effect of&amp;#160; a pixel's&amp;#160; neighboring land cover (including the type of vegetation and no-burnable area) on the output susceptibility map is investigated. In order to assess the&amp;#160; performance&amp;#160; of the model, the spatial-cross validation has been carried&amp;#160; out, trying&amp;#160; out different&amp;#160; number of folders. Susceptibility maps for the two fire seasons (the&amp;#160; summer&amp;#160; and&amp;#160; the winter&amp;#160; one) were finally computed&amp;#160; and validated. The&amp;#160; resulting&amp;#160; maps show&amp;#160; higher susceptibility zones , developing closer to the coast in summer and along the interior part of&amp;#160; the region in winter. Such zones matched well with the testing burned area, thus&amp;#160; proving the&amp;#160; overall&amp;#160; good performance of the proposed method.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;REFERENCE&lt;/strong&gt;&lt;/p&gt;&lt;p&gt; Tonini M., D&amp;#8217;Andrea M., Biondi G., Degli Esposti S.; Fiorucci P., A machine learning based approach for wildfire susceptibility mapping. The case study of Liguria region in Italy. &lt;em&gt;Geosciences&lt;/em&gt; (2020, submitted)&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;