Integrating ecological indices and machine learning for bird-strike risk assessment at Murtala Muhammed International Airport, Lagos
DOI:
https://doi.org/10.5281/zenodo.20081885Keywords:
Bird strike collision, Ecological Risk Modeling, Random Forest algorithm, Bird Strike Risk Index, Aviation Safety Risk Value, Aircraft damageAbstract
This study integrated ecological indices and machine-learning modeling to quantify birdstrike risks at Murtala Muhammed International Airport (MMIA), Lagos, Nigeria. Field surveys recorded 27 bird species, while secondary data from the Federal Airports Authority of Nigeria (FAAN) provided historical birdstrike incidents (2012–2022). Ecological risk metrics, including Aviation Safety Risk Value (ASRV), Hazard Score (ASRV × Abundance), and Bird Strike Risk Index (BSRI) were computed to evaluate species-specific hazards. Correlation analysis revealed a weak relationship between ASRV and Abundance (r = 0.26), and strong positive relationships between BSRI and Abundance (r = 0.91) and between BSRI and ASRV (r = 0.58), confirming the internal consistency and mathematical structure of the computed indices. Historical regression analysis indicated a significant upward trend in annual birdstrike frequency (R² = 0.61, p < 0.01), averaging an increase of 8.2 strikes yr⁻¹ between 2012 and 2022. Principal Component Analysis and K-means clustering identified three statistically distinct risk groups explaining 86.7 % of total variance, dominated by high-abundance and high-hazard species, particularly Black-headed Heron (Ardea melanocephala) and Owl. Machine-learning models were used to classify and rank species hazard levels. The Reduced Model (based on ASRV and abundance) achieved an R² of 0.84, indicating that species can be consistently ranked in relative hazard categories based on ecological traits rather than independently predicting bird-strike events. These findings demonstrate that species-level hazard at MMIA is ecologically structured, while aerodrome-level strike frequency exhibits a statistically increasing trend. Integrating ecological metrics with machine-learning analytics provides a quantitative framework for proactive bird hazard management and strengthens compliance with ICAO wildlife-hazard mitigation standards.
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