Forest fire susceptibility modeling in the Eastern Mediterranean: A machine learning assessment

Annual wildfires present a growing threat to the endangered forests of the Eastern Mediterranean. In this research, high-resolution forest fire susceptibility maps in Tartous Governorate were developed by employing four distinct machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and k-Nearest Neighbor (KNN), to evaluate and contrast their predictive efficacy in Tartous Governorate of western Syria. A comprehensive dataset comprising 1,500 historical fire events, along with 15 environmental variables, was utilized for the analysis. This analysis utilized a range of data sources, encompassing remote sensing data as well as datasets from official governmental sources. Among the models assessed, RF demonstrated the highest predictive accuracy at 94.33%. Random forest classifier map indicated that 38.78% of the study area is categorized as being at high and very high risk of forest fire. The geographical characteristics of these areas, which are predominantly situated in mountainous and hard-to-reach regions, underscore the inherent sensitivity and the necessity of formulating a thorough fire management strategy for the region. Among the studied parameters, precipitation, altitude, and NDVI were identified as the primary determinants affecting fire vulnerability. The findings detailed in this study offer substantial insights into the various factors influencing forest fires in the examined area. Coupled with the generated susceptibility map, these results can aid in formulating effective fire management strategies applicable to this region and comparable Eastern Mediterranean forest ecosystems.