Ersoy, IzzetUnsal, EmreGursoy, Onder2025-05-042025-05-0420252071-1050https://doi.org/10.3390/su17051971https://hdl.handle.net/20.500.12418/35176Forest fires pose significant environmental and economic risks, particularly in fire-prone regions like the Mediterranean coast of T & uuml;rkiye. This study presents a comprehensive Forest Fire Danger Assessment System (FoFiDAS), by integrating Geographic Information Systems (GIS), a literature-based model, the Analytical Hierarchy Process (AHP), and machine learning (ML) to improve forest fire danger classification. Both models integrate 13 key parameters identified through the literature. A comparison of these models revealed 53% overlap in fire danger classifications. While the AHP model, based on expert-weighted assessment, provided a more structured and localized classification, the literature-based model relied on broader scientific data but lacked adaptability. Pearson correlation analysis demonstrated a strong correlation between fire danger classifications and historical fire occurrences, with correlation scores of 0.927 (AHP) and 0.939 (literature-based). Further ROC analysis confirmed the predictive performance of both models, yielding AUC values of 0.91 and 0.9121 for the literature-based and AHP models, respectively. Five ML algorithms were used to validate classification performances, with Artificial Neural Network (ANN) achieving the highest accuracy (86.5%). The accuracy of the ANN algorithm exceeded 0.93 for each danger class, and the F1-Score was above 0.85. FoFiDAS offers a reliable tool for fire danger assessment, supporting early intervention and decision making.en10.3390/su17051971info:eu-repo/semantics/openAccessforest firefire danger analysisfire danger mappinggeographical information systemmachine learninganalytical hierarchy processAHP-GIS integrationArticle1752-s2.0-86000572018Q1WOS:001443509400001Q2