Akademik Veri Yönetim Sistemi
International Journal of Applied Earth Observation and Geoinformation, cilt.146, 2026 (SCI-Expanded, Scopus)
Forest fires pose a significant threat to ecosystems, biodiversity, and human settlements. This study focuses on the Arsin Forest Sub-district Directorate, located in Trabzon, Türkiye, with the aim of developing wildfire susceptibility maps using machine learning techniques. To improve the completeness of the wildfire inventory dataset, official fire records from 2013 to 2022 were integrated with active fire pixel data obtained from the Fire Information for Resource Management System (FIRMS) for the period 2001–2012. Five machine learning models Random Forest (RF), Artificial Neural Network (ANN), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Deep Neural Network (DNN) were employed to generate susceptibility maps. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC curves, and AUC-ROC metrics. Seventeen wildfire conditioning factors, categorized into four groups (topographic, meteorological, stand-related, and anthropogenic), were used to assess fire risk. Unlike previous studies, this research incorporates a region-specific anthropogenic variable: proximity to hazelnut cultivation. Feature importance scores were computed to determine the influence of each factor on fire occurrence. Additionally, SHapley Additive exPlanations (SHAP), accompanied by graphical analysis, were used to interpret the relationships between predictors and fire events. Areas of very high fire susceptibility covering 4.62% and 4.86% of the study area were identified by the RF and GBM models, both achieving an accuracy of 0.98.