Anticipating Wildfire Behavior: Fire Spread Modelling Case Studies in Greece and the U.S.

Wildfires, intensified by shifting climate patterns, present a growing challenge globally. This contribution focuses on fire spread modeling as an approach to strengthen both prevention and response strategies. By combining physical modeling with ML optimized parameter optimization, fire spread simulations offer practical insights into fire behavior across diverse environmental scenarios. The capabilities are illustrated using three 3 example case studies across different regions and conditions: two in Athens, Greece, and one in the United States.

The Fire Propagation Simulation can be applied during ongoing events to anticipate the fire’s course and support timely interventions. It can also be used in hypothetical scenarios to assess the impact of prevention strategies and refine risk reduction plans.

The research addresses key challenges, including integrating firefighting tactics into simulations and overcoming uncertainties in environmental datasets. By incorporating multimodal datasets, this study aims to enhance our understanding of fire dynamics and offers actionable strategies for managing wildfire risks effectively.