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<p>Wildfire risk assessment is essential for managing and mitigating the effects of wildfires, especially in regions frequently affected by severe fires. This study focuses on optimizing the National Fire Danger Rating System (NFDRS) by creating 11 custom fuel models tailored to the climatic and vegetation conditions of the Golestan province, NE Iran. These models were developed by sampling 375 plots in 125 homogeneous zones defined in 20,367&#xa0;km<sup>2</sup>. The number of custom fuel models was determined by K-means analysis with relative squared Euclidean distances and the silhouette method using data collected from the homogeneous zones. The NFDRS outputs include the Spread Component (SC), Energy Release Component (ERC), and Burning Index (BI), with fire danger classes identified using the Static Fire Danger Index. The models were evaluated using Overall Accuracy, Kappa, Sorensen, True Positive Rate, False Positive Rate, and Area under the Curve (AUC) by calculating p percentage (i.e., the combined ERC and BI Percentiles) for a set of fire point and non-fire point in the study area for 2012–2023. The results revealed significant variability in fuel properties among vegetation types, affecting SC and ERC values. The custom fuel models outperformed the standard NFDRS models in the accuracy of wildfire risk assessment, with Overall Accuracy (0.85), Kappa (0.78), and AUC (0.92) compared to the NFDRS's 0.70, 0.65, and 0.80, respectively. This study demonstrated that custom fuel models significantly improve the accuracy of wildfire risk assessment and provide fire managers with more reliable information to support decision-making, enhance preparedness, and optimize resource allocation. These models can help mitigate the environmental and socioeconomic impacts of wildfires by supporting more effective prevention, preparedness, and response strategies. The research underscores the importance of region-specific, customized fuel models to enhance wildfire risk accuracy.</p>

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Optimizing NFDRS by developing custom fuel models for wildfire risk assessment in Golestan Province, NE Iran

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Mhd. Wathek Alhaj-Khalaf,
  • Shaban Shataee Jouibary,
  • Roghayeh Jahdi,
  • William M. Jolly

摘要

Wildfire risk assessment is essential for managing and mitigating the effects of wildfires, especially in regions frequently affected by severe fires. This study focuses on optimizing the National Fire Danger Rating System (NFDRS) by creating 11 custom fuel models tailored to the climatic and vegetation conditions of the Golestan province, NE Iran. These models were developed by sampling 375 plots in 125 homogeneous zones defined in 20,367 km2. The number of custom fuel models was determined by K-means analysis with relative squared Euclidean distances and the silhouette method using data collected from the homogeneous zones. The NFDRS outputs include the Spread Component (SC), Energy Release Component (ERC), and Burning Index (BI), with fire danger classes identified using the Static Fire Danger Index. The models were evaluated using Overall Accuracy, Kappa, Sorensen, True Positive Rate, False Positive Rate, and Area under the Curve (AUC) by calculating p percentage (i.e., the combined ERC and BI Percentiles) for a set of fire point and non-fire point in the study area for 2012–2023. The results revealed significant variability in fuel properties among vegetation types, affecting SC and ERC values. The custom fuel models outperformed the standard NFDRS models in the accuracy of wildfire risk assessment, with Overall Accuracy (0.85), Kappa (0.78), and AUC (0.92) compared to the NFDRS's 0.70, 0.65, and 0.80, respectively. This study demonstrated that custom fuel models significantly improve the accuracy of wildfire risk assessment and provide fire managers with more reliable information to support decision-making, enhance preparedness, and optimize resource allocation. These models can help mitigate the environmental and socioeconomic impacts of wildfires by supporting more effective prevention, preparedness, and response strategies. The research underscores the importance of region-specific, customized fuel models to enhance wildfire risk accuracy.