Deep Learning for Wildfire Susceptibility Mapping in a Mediterranean Region: Towards an Operational Comparison with the Fire Weather Index

Wildfires in the Mediterranean are expected to become increasingly frequent and intense under climate change and increasing drought conditions. Owing to the growing computational capabilities of modern GPUs, deep learning approaches are increasingly being adopted for wildfire modeling and often outperform conventional methods. This study presents a deep feed-forward neural network, named the Wildfire Neural Network (WNN), designed to generate daily wildfire susceptibility maps. The model integrates meteorological variables, FWI-derived indicators, geomorphological attributes, land-use information, and anthropogenic proxies. WNN was trained and validated using multi-year data from Calabria, southern Italy, a Mediterranean wildfire hotspot. Compared with the operational Fire Weather Index baseline, WNN achieved higher AUROC values and greater spatial information content, with AUROC improvements of approximately 7% to 21% considering the historical configurations.