Wildfire risk assessment and monitoring remain critical challenges in Mediterranean ecosystems, particularly in North Africa, where recurrent ignition patterns threaten forested landscapes and rural livelihoods. This study presents an integrated, simulation-based framework that links retrospective satellite fire observations with spatial wildfire risk estimation and wireless sensor network (WSN) deployment analysis in Tunisia. Historical NASA FIRMS VIIRS fire detections (2022–2025) are analyzed using multiple spatial clustering algorithms to identify persistent ignition corridors. Supervised learning models, evaluated under spatial cross-validation and strict temporal splits, are employed to generate static wildfire occurrence likelihood surfaces. A ConvLSTM model is examined as an exploratory component to assess short-term spatiotemporal patterns in fire activity. Wildfire risk information is subsequently incorporated into a simulation-based, multi-objective WSN deployment framework solved using Particle Swarm Optimization (PSO). Simulation results obtained under idealized assumptions indicate that hotspot-informed WSN deployment strategies can reduce average inter-node distance by approximately 28% and communication energy consumption by approximately 35% relative to a uniform grid baseline. While the proposed framework does not constitute an operational early-warning system, it provides a reproducible methodology for translating satellite-derived wildfire risk information into monitoring infrastructure planning, with relevance for fire-prone regions in Tunisia and similar Mediterranean environments.
Mejri et al. (Thu,) studied this question.