Wildfire Susceptibility in Italy: High-Resolution Mapping for Power Grid Resilience

The increasing number of wildfires in Italy presents a growing challenge for environmental protection and infrastructure resilience. Among the most vulnerable assets is the high-voltage transmission network: during wildfire events, in fact, overhead lines must often be preemptively deactivated to facilitate aerial and ground-based firefighting and to preserve infrastructure integrity and grid stability. This necessity creates a critical conflict between emergency response requirements and the continuity of electricity supply.

While anthropogenic activities and human negligence remain the primary drivers of ignition, the meteorological conditions leading to fire spread have worsened in recent years due to persistent summer heatwaves and prolonged droughts. To monitor and predict wildfire danger, various meteorological indices have been developed, most notably the Canadian Fire Weather Index (FWI). However, while these indices are essential for daily operational monitoring, they are inherently limited by not considering fuel availability and terrain characteristics. Consequently, high FWI values may be recorded in areas with no combustible biomass, such as urban areas, highlighting the limits of purely weather-based fire danger assessments.

To improve fire danger characterization, a susceptibility map was developed on a 100-meter resolution grid covering the entire Italian territory. To achieve this, a random forest model was trained on non-meteorological, high-resolution data using a balanced dataset constructed from areas burned between 2010 and 2023, and an equal number of randomly sampled non-fire locations. These features included land use, topography (elevation, slope, and aspect), latitude, and proximity to critical infrastructure (roads and power lines). The model demonstrated high predictive performance, achieving an accuracy of 0.95 on a 30% hold-out test sample; feature importance analysis revealed that latitude, elevation, and land-use class are the primary drivers of fire susceptibility. Finally, the model has been applied across the entire Italian Peninsula, yielding a high-resolution map of burning probability for each grid cell.

To evaluate its operational effectiveness, the susceptibility map was validated against two case studies where wildfires directly caused the deactivation of critical power lines. The results demonstrate that the map significantly refines the spatial accuracy of coarser meteorological alerts based solely on the FWI. By integrating fuel and topographic data with weather-based indices, the model successfully narrows the focus to specific high-risk segments of the grid, thereby reducing 'false alarm' areas and providing a more targeted decision-support tool for transmission system operators.

This susceptibility map provides an important foundation for a comprehensive wildfire alert system, bridging the gap between broad meteorological forecasts and local-scale infrastructure needs. By refining established weather indices with high-resolution environmental and topographic data, the model allows for a level of situational awareness compatible with the needs of power grid operators within the growing challenges of Mediterranean climate.