## 2 Materials and Methods
### Methodology  
#### 2.2.1 Overview  
The methodology of this study involves the integration of two different models, namely BEYOND's Machine Learning model for daily fire risk forecasting [30] and the parameterized FlamMap fire spread model that is a combination of FlamMap [13] and FireHUB, which is an integrated early warning and near-real time monitoring system developed by the Operational Unit BEYOND of NOA [31]. Both of these models were adapted and calibrated to fit the local conditions and characteristics of the study area. Moreover, it offers the only high-detailed integrated approach in the Greek territory. In  
Figure 1: Study area.  
particular, it stands out from other existing solutions by emphasizing the establishment of pre-event actions (e.g., road cleaning) to mitigate fire risk effectively.  
The methodological framework was developed considering already established and applied methodologies EMSN041, EMSN059, and FireHub [30; 32], and an extensive literature review [8; 33; 34]. Building upon this foundation, the methodology employed in this study utilizes a next-day fire risk model, which has been trained on historical data spanning from 2010 to 2018 and has been applied for daily predictions during the years 2019 to 2021, enabling precise assessment of ignition hazards within 100 \(\times\) 100 m subareas of the area of interest (AOI). Furthermore, an integrated custom Flamap model is employed to simulate potential wildfire behavior originating from locations identified by the fire risk model. To ensure the methodology's reliability, real-world validation is conducted through on-site visits, during which specific risks such as homes near forests with wooden structures, overgrown yards, power line/tree branch intersections, and poorly maintained roads with steep inclines and dead-end sections are systematically identified. This holistic approach not only enhances the robustness of the methodology but also significantly contributes to more effective wildfire risk assessment and mitigation strategies. The flowchart of the methodology is presented in Figure 2.  
In a nutshell, various simulations of spatio-temporal fire spread were performed for the area of interest. The worst-case fire scenarios, in terms of time and extent propagation, were selected and combined to render the overall hazard for the area. In parallel, the region's vulnerability was composed and classified (into 5 classes from very low to very high according to the Natural Breaks classifier) by co-assessing the population (density and age) and building characteristics (construction material) of the area of interest. In addition, the area's land value zones (also categorized into 5 classes) were used as a qualitative exposure proxy of the possible economic impact on the region in case of fire. The coupling of the above produced a high-detail (building-block-level) fire-risk map of the area, based on which the high-risk areas were identified and visited through exhaustive field campaigns. The knowledge acquired from the field visits helped the iterative process of validating and updating the risk maps, the identification of critical points and important  
Figure 2: Flowchart of the methodology.  
sites, and finally resulted in the development of operational management plans and fire prevention suggestions.