Wildfires constitute an extremely serious social and environmental issue in the Mediterranean region, with impacts on human lives, infrastructures and ecosystems. It is therefore important to produce susceptibility maps for wildfire management. The wildfire susceptibility is defined as a static probability of experiencing wildfire in a certain area, depending on the intrinsic characteristics of the territory. In this work, a machine learning model based on the Random Forest Classifier algorithm is employed to obtain national scale susceptibility maps for Italy at a 500 m spatial resolution. In particular, two maps are produced, one for each specific wildfire season, the winter and the summer one. Developing such analysis at the national scale allows for having a deep understanding on the wildfire regimes furnishing a tool for wildfire risk management. The selected machine learning model is capable of associating a data-set of geographic, climatic, and anthropic information to the synoptic past burned area. The model is then used to classify each pixel of the study area, producing the susceptibility map. Several stages of validation are proposed, with the analysis of ground retrieved wildfire databases and with recent wildfire events obtained through remote sensing techniques.

Background: Wildfires are a growing threat to many ecosystems, bringing devastation to human safety and health, infrastructure, the environment and wildlife. Aims: A thorough understanding of the characteristics determining the susceptibility of an area to wildfires is crucial to prevention and management activities. The work focused on a case study of 13 countries in the eastern Mediterranean and southern Black Sea basins. Methods: A data-driven approach was implemented where a decade of past wildfires was linked to geoclimatic and anthropic descriptors via a machine learning classification technique (Random Forest). Empirical classification of fuel allowed linking of fire intensity and hazard to environmental drivers. Key results: Wildfire susceptibility, intensity and hazard were obtained for the study area. For the first time, the methodology is applied at a supranational scale characterised by a diverse climate and vegetation landscape, relying on open data. Conclusions: This approach successfully allowed identification of the main wildfire drivers and led to identification of areas that are more susceptible to impactful wildfire events. Implications: This work demonstrated the feasibility of the proposed framework and settled the basis for its scalability at a supranational level.

Wildfires pose significant threats to both ecosystems and socio-economic assets, particularly under changing climate conditions. This paper presents a comprehensive, data-driven approach for wildfire risk assessment that integrates machine learning modeling, fuel-type classification, and economic loss estimation. First, a Random forest-based susceptibility model is developed to identify the probability of fire spread, using diverse predictors such as climate indices, topographic parameters, and vegetation continuity. The model is calibrated at a pan-European scale and then tailored at national levels to capture interannual variability. Next, wildfire hazard is determined by combining susceptibility output with aggregated vegetation classes to approximate potential fire intensity. This susceptibility-hazard combination highlights the range of wildfire behaviors, from low-intensity surface fires in grasslands to high-intensity crown fires in coniferous forests. To account for potential economic impacts, the approach incorporates exposure data—buildings, forests, and critical infrastructure, along with vulnerability tables that translate hazard intensity into monetary losses. Average Annual Loss is then derived by pairing hazard-based probabilities with these exposure and vulnerability layers. The results comprehensively quantify potential financial impacts. By merging detailed ML susceptibility modeling with explicit hazard and loss calculations, this study offers a scalable, robust tool for policymakers and land managers seeking to mitigate wildfire risks in an era of intensifying climate extremes.

Preserving cultural heritage from natural hazards is of paramount importance due to the role that cultural heritage plays in supporting community resilience and economic activities. Therefore, being able to map the risk faced by cultural heritage, especially in a multi-risk perspective, is useful to provide a policy-making tool which highlights hotspot areas.In this work, we present a preliminary version of a multi-risk map of cultural heritage at the national scale considering flood, earthquake, landslide and wildfire hazards, in Italy. The exposure dataset provided by the Italian Ministry of Culture counts ca. 180-thousand-point elements grouped into three classes (Architecture, Archaeological, green open space) and 393 typologies (e.g., archive, arch, abbey etc.). In order to categorize the elements in a more convenient fashion, a taxonomy of 30 classes is defined by intersecting common geometric properties of the elements (e.g., tall, subterranean, equidimensional etc.) with the type of structure (e.g., defensive architecture, buildings potentially containing valuable items, etc.). For each hazard, a qualitative classification of the vulnerability of each taxonomic element has been assigned based on expert judgement and literature studies. As source of hazard information, the national landslide inventory (provided by Istituto Superiore Per La Protezione E La Ricerca Ambientale), the flood risk management plans (by Hydrographic District Authorities), the peak ground acceleration map (by Istituto Nazionale di Geofisica e Vulcanologia) and the wildfire hazard map (CIMA Research Foundation) have been adopted. Single risk hotspots and a multi-risk map have been produced by preliminary selecting the municipal boundaries as scale of aggregation of the results.To the best of our knowledge, this is the first work attempting to merge national-scale datasets to produce a multi-risk map for cultural heritage. Our ambition for the future is to extend this method to other types of risk and countries. Acknowledgments: This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).

Fuel characterization plays a central role in every phase of wildfire risk management, from identifying priority areas for prevention to supporting wildfire danger evaluation and simulating fire spread. Despite their importance, producing fuel maps that are both up to date and spatially extensive remains a persistent difficulty in fire science. Highly detailed information on fuel structure and composition would improve fire behavior simulations and danger assessment, but collecting such data is often difficult or impractical at the required level of detail. As a result, wildfire management must often rely on simplified representations that trade detail for feasibility. This raises a critical operational question: can fuel classification systems be designed to remain effective and reliable while being simple enough for large-scale, operational applications? In response to this need, the CIMA Foundation has developed an operational fuel classification methodology tailored to civil protection requirements. The approach integrates land cover data, vegetation typologies, and environmental variables with expert-driven rules and machine learning–based wildfire susceptibility analyses. Rather than aiming for exhaustive fuel descriptions, the method focuses on capturing the most relevant characteristics for operational decision-making that is, the susceptibility of the territory to wildfire spreading. Originally conceived for static fire susceptibility mapping at multiple spatial scales - ranging from regional to pan-European - the methodology has since been expanded to account for drought conditions. This enhancement allows fuel susceptibility to vary over time, producing dynamic maps that better represent seasonal changes in vegetation flammability. Such temporal variability is especially important in the context of climate change, where prolonged droughts combined with extreme weather can amplify wildfire severity. Addressing these compound drivers is a key requirement for operational wildfire forecasting in civil protection systems. The resulting fuel maps serve as a core input for the RISICO wildfire danger forecasting model, developed by CIMA Foundation and used by the Italian Civil Protection Department, regional authorities, and international partners. Dynamic fuel representations have been tested in pre-operational settings at the regional level in Italy, as well as in international applications, demonstrating their usefulness in supporting wildfire danger bulletins. In parallel, the static fuel map has been employed as an input for the PROPAGATOR fire spread model, extending its applicability across different components of the wildfire risk management cycle. Although intentionally less detailed than some advanced fuel classification schemes, this approach has proven fit for purpose in operational contexts. It offers a pragmatic compromise between scientific rigor and usability, enabling the effective integration of scientific knowledge into decision-support tools for wildfire management.

Susceptibility maps constitute a useful tool for wildfire management