An Artificial Intelligence-Driven Conceptual Model for Wildlife Strike Risk Quantification and Aircraft Airworthiness Impact Assessment at High-Traffic African Airports

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@article{1718482,
      author = {Oluwabukola Oluwapelumi Adeyelu},
      title = {An Artificial Intelligence-Driven Conceptual Model for Wildlife Strike Risk Quantification and Aircraft Airworthiness Impact Assessment at High-Traffic African Airports},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {1},
      pages = {339-368},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718482.pdf},
      abstract = {Background: Wildlife strikes represent one of the most significant and underquantified safety risks at African international airports, where dense and diverse avifaunal populations, limited wildlife monitoring infrastructure, and rapidly growing traffic volumes combine to create elevated and poorly characterized strike risk environments that existing reactive management frameworks are structurally inadequate to address. The absence of locally calibrated quantitative risk assessment tools forces aerodrome wildlife management teams to make deterrence deployment decisions based primarily on subjective observation and limited historical strike records rather than on probabilistic risk intelligence derived from real-time sensor data integrated with ecological and operational context variables
Methods: This paper proposes an Artificial Intelligence-Driven Wildlife Strike Risk Quantification Model (AI-WSRQM) that integrates machine learning classification of radar and acoustic sensor data with historical strike database pattern analysis and aircraft component vulnerability mapping to generate real-time wildlife strike risk indices at high-traffic African airports. The model architecture specifies data ingestion, preprocessing, machine learning classification, alert generation, and airworthiness integration modules, with design parameters calibrated to the ecological, operational, and regulatory environment of Nigerian civil aviation and applicable ICAO standards
Results: The AI-WSRQM generates probabilistic strike risk scores stratified by wildlife species group, flight phase, runway orientation, and season, enabling aerodrome wildlife management teams to implement targeted deterrence interventions at the times and locations of highest strike probability. Integration with maintenance reporting systems enables progressive airworthiness impact assessment that accumulates strike history across aircraft registrations for fleet-level vulnerability analysis
Conclusion: The AI-WSRQM advances African aviation wildlife management beyond reactive strike reporting toward proactive, sensor-informed risk quantification compatible with modern Safety Management System proactive safety assurance requirements and applicable ICAO Annex 14 and Doc 9137 standards. Implementation at Nigerian international aerodromes would constitute a significant advancement in wildlife hazard management capability},
      keywords = {Wildlife Strike, Artificial Intelligence, Risk Quantification, Airworthiness, African Airports, Bird Strike Prevention, Safety Management System, Radar Tracking, Machine Learning, NCAA Nigeria},
      month = {July},
      doi = {https://doi.org/10.64388/IREV4I1-1718482}
  }