Forest Fire Monitoring UAV System Using Forest PSO-GA Algorithm

This study introduces an enhanced Forest PSO-GA algorithm for forest fire monitoring, integrating Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and forest wind dynamics, while considering the impact of terrain variations on energy con-sumption, into an adaptive search framework. By incorporating wind-driven fire propagation and smoke diffusion models into a cellular automata simulation platform, the algorithm effectively evaluates its performance and further accounts for elevation and energy consumption. This enables more accurate simulation of fire and smoke spread, ensuring efficiency and sustainability in remote forest areas. Simulations using data from the Harbin Liangshui Forest show that the enhanced Forest PSO-GA out-performs APSO, AFSA, and PSO-PID in search speed by 91.34%, 340.89%, and 52.21%, respectively. It achieves an average localization accuracy of 9 meters (±1.2 meters), which is sufficient for the precise deployment of fire-extinguishing devices. The algo-rithm also reduces the search area by 35.4-72.3% and converges within 50 iterations 80% of the time, representing a 28.7% efficiency gain over PSO-PID. Additionally, the algo-rithm boasts a success rate of 94.3% and a 61.8% improvement in wind resistance, ef-fectively supporting pre-disaster warnings and early fire detection. These advance-ments significantly enhance fire detection accuracy, reduce the burden on forest fire prevention efforts, and improve precision firefighting and ecological recovery capabil-ities, offering a highly efficient and reliable solution for forest fire management.