AI and ML Enhance Aviation Safety Prediction and Prevention

This study explores the application of advanced modeling and data analytics, specifically machine learning (ML) and natural language processing (NLP), to enhance aviation safety. Despite the abundance of historical safety data, its full potential for incident prediction and prevention has been underutilized. The research addresses this by analyzing diverse datasets from sources like Socrata, ATSB, NTSB, and ASN. The methodology involves leveraging existing ML models, including deep learning and transformer-based architectures, alongside NLP techniques to mine incident narratives for patterns contributing to accidents and near-misses. Topic modeling is also employed to extract meaningful themes from unstructured safety reports, improving the interpretability of incident analysis. The findings offer data-driven insights for aviation regulators, airlines, and policymakers, supporting efforts to minimize risks and integrate AI into safety management.