Forest fires are the main cause of forest degradation in Algeria, with serious consequences for the environment and the local economy. The variety of traditional models and methods used for fire prevention and suppression poses challenges in terms of specialization, precision, updating, and validation. These techniques and procedures are often slow and less reliable due to the complexity and diversity of forest ecosystems. Research in this field has demonstrated the effectiveness and speed of using remote sensing and Geographic Information Systems (GIS) to map the vulnerability of forest fires in the eastern Aurès region in Algeria, where the Turkish model (Erten) was applied in this study. This model integrates five key factors essential for assessing forest fire vulnerabilities, which include slope, aspect, the vegetation, proximity to roads, and proximity to human settlements. The results revealed that the region faces vulnerabilities ranging from low to very high. Where 691.71 km² (41.39%) of the surface area is in a high to very high vulnerability zone. The validity of these results was verified in two different ways. Initially, field surveys were carried out using reference data to pinpoint the regions of fire outbreaks in 2022 and compare the findings with the results obtained. In this study, it was discovered that 33 of the 47 fire points, roughly 70.2%, are situated in regions that are categorized as high and very high vulnerability for forest fires, while the outcomes were good. The second method was statistical validation based on the ROC analysis, which yielded an AUC value of 0.747, indicating good predictive performance. Additional accuracy metrics (overall accuracy = 64%, precision = 0.61, recall = 0.70) confirmed the model’s reliability in distinguishing between fire and non-fire areas. The results obtained are promising and contribute to improved forest fire prevention and informed decision-making.
Khelali et al. (Sun,) studied this question.