# Utilizing AI for Aviation Post-Accident Analysis Classification

arXiv: https://arxiv.org/abs/2506.00169 · HTML: https://mlsanity.com/paper/2506.00169/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 10 of 10)

- [Machine Learning for Air Transport Planning and Management](https://mlsanity.com/paper/2112.01301/) (2021)
- [Topic Modeling Analysis of Aviation Accident Reports: A Comparative Study between LDA and NMF Models](https://mlsanity.com/paper/2403.04788/) (2024)
- [Comparative Analysis of Topic Modeling Techniques on ATSB Text Narratives Using Natural Language Processing](https://mlsanity.com/paper/2501.01227/) (2025)
- [Applications of natural language processing in aviation safety: A review and qualitative analysis](https://mlsanity.com/paper/2501.06210/) (2025)
- [Sequential Classification of Aviation Safety Occurrences with Natural Language Processing](https://mlsanity.com/paper/2501.06490/) (2025)
- [Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences](https://mlsanity.com/paper/2501.06564/) (2025)
- [Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports](https://mlsanity.com/paper/2501.07923/) (2025)
- [Comparative Study of Deep Learning Architectures for Textual Damage Level Classification](https://mlsanity.com/paper/2501.01694/) (2025)
- [Exploring Aviation Incident Narratives Using Topic Modeling and Clustering Techniques](https://mlsanity.com/paper/2501.07924/) (2025)
- [Phase of Flight Classification in Aviation Safety using LSTM, GRU, and BiLSTM: A Case Study with ASN Dataset](https://mlsanity.com/paper/2501.07925/) (2025)

## Built on by (most cited first, 1 of 1)

- [Semantic Causal-Factor Inference from Aviation Incident Narratives Using A Variational Autoencoder with Cosine-Similarity-Based Reconstruction](https://mlsanity.com/paper/2610.04472/) (2026)

## Abstract (arXiv)

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.