Multi-modal AI-Enabled UAV Network for Fog Dispersal and Runway-Visibility Enhancement at an International Airport

Abstract Fog-related flight disruption is costing big international airports more than Rs 2.5 crores for each such event, while the traditional countermeasures, chemical seeding and thermal heating, are expensive, slow and environmentally damaging. This paper proposes the first field-validated airport fog dispersal autonomous UAV system that combines deep reinforcement learning with targeted UV-C photolysis technology. Conventional ways of fog dispersal take 30–45 min for runway clearance, cost Rs 15,000 per operation and produce 500 kg of CO2 emissions. These strategies evaporate fog droplets without tackling the condensation nuclei that are causing them and so the fog can quickly reform. We use a 4-UAV swarm with UV-C LED arrays (254 nm wavelength) for the degradation of hygroscopic aerosols which act as cloud condensation nuclei (CCN). Unlike thermal approaches that only evaporate droplets, our photolysis-based approach can reduce the efficiency of CCN by 35–45% so that the fog does not re-form. A deep Q-Network (DQN), based on 625-256-256-8 architecture, autonomously coordinates swarm positioning based on real-time sensor fusion from LiDAR (25 × 25 m resolution), thermal imaging (640 × 480 at 30fps) and meteorological arrays. 96% accuracy of fog detection. Our operational flights took place at Sri Guru Ram Dass Jee International Airport Amritsar, India, with 120 flights starting from November 2024 till March 2025. Results show: 83.1% reduction in time of fog clearance (from 30 to 5.06 min), 80% improvement of runway visibility range (from 450 to 810 m), 95% reduction in cost (from Rs 800 to Rs 15000 per sortie), 96% reduction in CO2 emission (from 20 to 500 kg per operation).Randomized complete block design using Friedman analysis (kh2 = 128.45, p < 0.001, Cohen’s d effect sizes of 4.85–7.92 show very large practical significance for all of the metrics. Zero incidents during 120 flights with ground exposure from UV-C (0.008 mJ/cm 2 ) 375x below ICNIRP occupational limits. Real-time DQN inference latency (183+-27ms) is the aviation safety-critical requirement (< 250ms). This research sets up a scalable paradigm for fog management at fog-prone airports anywhere in the world economically and environmentally and the potential savings is Rs. 2.5 Crores every year at major international airports.