Geostatistical modeling of meteorological condition for a decision support system in wildfire resilience management

Abstract This study aims to develop a systemic method for investigating and modeling the parameters that determine wildfire risk and its effects on infrastructure resilience. Through detailed analysis and quantification of predisposing factors contributing to fire risk, the research identifies and explores key variables impacting the risk, such as weather conditions, land morphology, and vegetation cover characteristics. By analyzing data and territorial knowledge, the study highlights the correlations between the main wildfire hazard factors to evaluate how these may influence wildfire likelihood and severity. The overall objective is to outline models and correlations that demonstrate how an increase in hazard factors is linked to a higher likelihood of ignition, to gain a deeper understanding of the impact of predisposing factors on land resilience and how such information can optimize disaster preparedness strategies, as well as risk and emergency management within the framework of Disaster Risk Management. The selected approach employs advanced methodologies for geostatistical modeling of parameters on a territorial and temporal scale and computational fluid dynamics simulations to predict fire risk scenarios, thereby improving understanding of wildfire development processes and supporting effective risk management strategies. Through the analysis of risks associated with wildfire hazards, the research aims to guide policymakers and communities in adopting proactive measures to minimize the impact of future events. The research emphasizes the significance of parameter analysis in understanding wildfire events, using the example of Ischia Island in southern Italy, while also highlighting the parameters contributing to hazards in similar Mediterranean regions.