Advances in Artificial Intelligence and Data-Driven Wildlife Hazard Monitoring and Incident Reduction at International Airports in West Africa

Background: Wildlife strikes remain one of the most significant and underestimated operational safety hazards at West African international airports, where the biodiversity and ecological richness of tropical and subtropical environments creates wildlife hazard exposure substantially higher than at comparable airports in temperate climate regions, and where the organizational and technological resources available for wildlife hazard management are substantially more constrained than at developed economy airports with mature wildlife management programs. The combination of high ecological exposure and limited management resource has produced wildlife strike rates at West African international airports that exceed ICAO recommended reduction targets and that contribute to significant aviation safety risk and economic loss through aircraft damage, delays, and diversions Methods: This paper presents advances in the application of artificial intelligence and data-driven monitoring approaches to wildlife hazard management at West African international airports, including validation of a computer vision wildlife detection system trained on West African bird and animal species under tropical climate and lighting conditions, development of a machine learning species classification algorithm for real-time strike risk assessment based on detected species characteristics, design of a predictive wildlife hazard risk model integrating ecological, meteorological, operational, and seasonal data streams, and cost-benefit analysis of AI wildlife monitoring system deployment at representative West African gateway airports Results: The AI wildlife detection system achieved a detection accuracy of 94.3 percent for priority high-risk species at Lagos Murtala Muhammed International Airport during the validation study, outperforming conventional visual observation monitoring by a margin of 28 percentage points in systematic observer comparison trials conducted across multiple weather and lighting conditions representative of the full range of operational monitoring requirements at Nigerian international airports Conclusion: Artificial intelligence and data-driven wildlife hazard monitoring represents a transformative advancement in the capability of West African airport operators to detect, assess, and respond to wildlife hazards in real time, addressing the fundamental detection and classification capability gap that limits the effectiveness of conventional wildlife management programs in the high-biodiversity, resource-constrained airport environments characteristic of West African international airport operations