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

Flight risk assessment tools (FRATs) aid pilots in evaluating risk arising from the flight environment. Current FRATs are subjective, based on linear analyses and subject-matter expert interpretation of flight factor/risk relationships. However, a 'flight system' is complex with non-linear relationships between variables and emergent outcomes. A neural network was trained to categorize high and low-risk flight environments from factors such as the weather and pilot experience using data extracted from accident and incident reports. Negative outcomes were used as markers of risk level, with low severity outcomes representing low-risk environments and high severity outcomes representing high-risk environments. Eighteen models with varied architectures were created and evaluated for convergence, generalization and stability. Classification results of the highest performing model indicated that neural networks have the ability to learn and generalize to unseen accident and incident data, suggesting that they have the potential to offer an alternative to current risk analysis methods.