Utilizing AI for Aviation Post-Accident Analysis Classification

TL;DR

This paper explores how AI, NLP, and Topic Modeling can automate and improve the analysis of aviation safety reports, leading to faster and more accurate safety insights.

Contribution

It reviews and compares deep learning and Topic Modeling techniques applied to aviation safety data, highlighting their effectiveness in classification and pattern detection.

Findings

AI and NLP significantly improve safety report analysis accuracy.

Deep learning models outperform traditional methods in classification tasks.

Topic Modeling uncovers recurring safety patterns.

Abstract

The volume of textual data available in aviation safety reports presents a challenge for timely and accurate analysis. This paper examines how Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) can automate the process of extracting valuable insights from this data, ultimately enhancing aviation safety. The paper reviews ongoing efforts focused on the application of NLP and deep learning to aviation safety reports, with the goal of classifying the level of damage to an aircraft and identifying the phase of flight during which safety occurrences happen. Additionally, the paper explores the use of Topic Modeling (TM) to uncover latent thematic structures within aviation incident reports, aiming to identify recurring patterns and potential areas for safety improvement. The paper compares and contrasts the performance of various deep learning models and TM techniques applied to datasets from the National Transportation Safety Board (NTSB) and the Australian Transport Safety Bureau (ATSB), as well as the Aviation Safety Network (ASN), discussing the impact of dataset size and source on the accuracy of the analysis. The findings demonstrate that both NLP and deep learning, as well as TM, can significantly improve the efficiency and accuracy of aviation safety analysis, paving the way for more proactive safety management and risk mitigation strategies.