Advancing Forest Fire Susceptibility Mapping in the Eastern Mediterranean Using Multi-Algorithm Machine Learning

Abstract

Forest fires are among the most severe natural hazards threatening ecological sustainability in the Eastern Mediterranean, with Syria being a prominent example where ongoing conflict has undermined forest management and monitoring systems. Despite numerous global studies on forest fire susceptibility, conflict-affected areas in the Eastern Mediterranean remain underrepresented in the scientific literature due to limited data and complex environmental and social conditions. In particular, few studies have systematically compared multiple machine learning algorithms for predicting forest fire susceptibility in these regions. To address this gap, this study evaluates the performance of six machine learning algorithms—Extreme Gradient Boost (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), and Linear Regression (LR)—using 3,589 fire events and 13 conditioning factors in the Latakia governorate, western Syria. Results indicate excellent predictive performance for all models, with XGBoost achieving the highest AUC of 0.993, followed by RF (0.992), KNN (0.977), MLP (0.975), SVM (0.968), and LR (0.963). These findings provide a robust basis for supporting fire management and mitigation strategies in Syria and offer insights applicable to other conflict-affected regions of the Eastern Mediterranean.