## 4 Discussion  
The proposed methodological framework presented in this study has demonstrated significant results, particularly when analyzing at the building-block level. However, like any comprehensive study, especially in complex fields like fire risk assessment, various challenges, limitations, and assumptions were encountered during its implementation. These key factors include data availability and quality, model limitations, the spatial and temporal resolution of datasets, accuracy assessment and validation, assumptions and simplifications made during the analysis, accessibility of digital information and field data, reliance on expert judgments, and the generalizability of the findings. Addressing these issues is crucial to enhance the robustness and applicability of the fire risk assessment methodology. The assessment of fire behavior plays a pivotal role in the strategic planning and effective management of wildfires. It provides critical insights into the potential spread and intensity of fires, enabling decision-makers to devise appropriate strategies to mitigate risks and protect communities and resources. In this context, two widely employed wildfire simulation models, FARSITE and FlamMap, stand out as valuable tools for comprehending and predicting fire behavior under diverse conditions [14; 48; 49]. [PERSON] et al. [50] calibrated the FARSITE fire spread model for wildfire simulation in northern Iranian forests using site-specific fuel models. The simulation results accurately replicated observed fire perimeters and behavior, particularly with the [PERSON] (2005) [40] standard fuel model. Another study [50] demonstrated the model's high potential for estimating spatial variability in fire spread and behavior, providing valuable insights for wildfire risk monitoring and management in the region. FARSITE offers a comprehensive approach by simulating both surface and crown fire behavior, allowing for a more holistic understanding of fire dynamics [50]. On the other hand, FlamMap primarily focuses on surface fire behavior, making it more suitable for smaller-scale assessments and tactical fire management. FARSITE, with its capacity for large-scale fire spread simulations over complex terrain and weather conditions, excels in addressing the challenges of wildfires at broader spatial scales. Both models complement each other, and their combined use provides a comprehensive toolkit for fire managers and planners to effectively address the complex challenges posed by wildfires. Although the FlamMap model is well accepted in the literature [51; 52; 53], it is unable to simulate fire scenarios where changes in wind direction and intensity occur. To address this limitation, multiple simulations based on various combinations of ignition points and wind conditions (direction and speed), were performed using the customized FlamMap. This approach ensured a more realistic understanding of fire behavior and could possibly enable decision-makers to implement targeted and effective wildfire management strategies to protect communities and resources. With regard to the facet of data availability and quality, the literature review provides valuable insights into the types of data that can enhance the accuracy and comprehensiveness of fire risk assessments. Some of these data, as mentioned, include more detailed economic information [54], distance and visibility from fire watch towers [54], proximity to water and roads [54], road networks [55], unoccupied housing units [56], military ground information [56], and the existence of fire hydrants [57]. We plan to incorporate most of the aforementioned parameters (depending on data availability) into future advancements of the methodology. The incorporation of data on population and building characteristics plays a crucial role in comprehending the potential impact of fire incidents and guiding targeted fire prevention strategies [54; 56].