Using neural networks to predict high-risk flight environments from accident and incident data.

Area of Science:

• Aviation safety
• Artificial intelligence in aviation
• Risk management

Background:

• Current flight risk assessment tools (FRATs) rely on subjective, linear analyses.
• The complexity of flight systems involves non-linear relationships and emergent outcomes.
• Existing methods struggle to capture the intricate dynamics of flight environments.

Purpose of the Study:

• To develop and evaluate a neural network model for categorizing high and low-risk flight environments.
• To explore the potential of artificial intelligence as an alternative to traditional FRATs.
• To leverage accident and incident data for objective risk assessment.

Main Methods:

• A neural network was trained using historical accident and incident report data.
• Flight factors like weather and pilot experience were used as input variables.
• Negative outcomes from reports served as risk level markers (low severity for low-risk, high severity for high-risk).

Main Results:

• Multiple neural network models with varied architectures were evaluated for performance.
• The highest-performing model demonstrated the ability to learn and generalize from unseen data.
• Classification results indicate successful categorization of flight risk levels.

Conclusions:

• Neural networks show significant potential as an alternative to current subjective flight risk assessment methods.
• The developed model can generalize to new data, suggesting robust applicability.
• AI-driven approaches can enhance the objectivity and accuracy of aviation risk analysis.