Wildfire Occurrence Probability in Talassemtane National Park (Morocco) Using Machine Learning Algorithms

Assessing the probability of forest fires by analyzing the main fire factors can provide forest managers with an essential basis for making crucial decisions on aspects such as prevention strategies, fuel management, fire safety measures, emergency plans and the deployment of firefighting teams. The main factors influencing fires, such as climatic conditions, vegetation characteristics, topographical factors and proximity to elements such as roads and residential areas, were considered in order to produce forest fire susceptibility maps. Machine learning algorithms have become a powerful tool for predicting the probability of forest fires. The aim of this study is to produce a forest fire susceptibility map using widely used machine learning models: Random Forest (RF) and Support Vector Machine (SVM). The study was conducted in Talassemtane National Park (TNP), located in the city of Chefchaouen in Morocco, covering an area of 58,950 ha and representing one third of the country’s biodiversity (Naoual, s.d.). The factors influencing fires that were taken into account were altitude, tree species, slope, exposure, LULC, NDVI, wind speed, soil moisture, temperature, precipitation and distance from roads. And we integrated forest fire occurrence data from 2001 to 2021 in the TNP into the training process. The accuracy of the fire susceptibility maps produced was assessed using the Area Under the Curve (AUC) value. After running the machine learning models, estimates were made for 257,499 points on the map, which were classified into five levels of fire susceptibility: very low, low, medium, high and very high. The results revealed that the fire susceptibility map generated by the RF model had better accuracy (AUC = 98.47) than that produced by the SVM model (AUC = 96.36). According to the probability maps, more than 7% (approximately 4,000 ha) of the forests in the study area were classified as having high or very high fire susceptibility levels.