What question did this study set out to answer?

The study aims to develop an AI-driven model to quantify wildlife strike risks and assess aircraft airworthiness at busy African airports.

June 7, 2026Iconic Research and Engineering Journals

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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Randomized trial examines wildlife strike risk quantification in aviation, indicating improved safety management strategies.

The study aims to develop an AI-driven model to quantify wildlife strike risks and assess aircraft airworthiness at busy African airports.
Proposed AI-Driven Wildlife Strike Risk Quantification Model integrates machine learning with sensor data.
Utilized historical strike data pattern analysis and aircraft vulnerability mapping.
Designed data processing and machine learning classification architecture calibrated to Nigerian civil aviation standards.
The model generates probabilistic risk scores stratified by species, flight phase, and season.
Enables implementation of targeted deterrence interventions based on real-time risk assessments.
Enhances airworthiness impact assessments through integration with maintenance reporting systems.

Oluwabukola Oluwapelumi Adeyelu (2020) studied this question.

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