Utilizing AI for Aviation Post-Accident Analysis Classification

2025-05-30

AI Summary

This study addresses the inefficiency of manual analysis for large-scale aviation safety reports. Methodologically, it proposes an AI-driven automated analytical framework integrating BERT-based pre-trained models with LSTM/CNN architectures to jointly classify aircraft damage severity and flight phases; it further employs LDA and NMF for cross-organizational topic modeling and conducts the first systematic comparative evaluation of deep learning versus topic modeling generalizability across multi-source datasets from NTSB, ATSB, and ASN. The key contribution lies in empirically revealing the critical impact of data scale and provenance on classification accuracy and introducing a novel joint analysis paradigm tailored to aviation safety. Experimental results demonstrate a 23.6% improvement in damage classification accuracy and significantly enhanced topic interpretability, thereby enabling proactive risk identification and evidence-based safety intervention decisions.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Security and Privacy: Large-scale security measurements

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.

Problem

• Automating analysis of aviation safety reports using AI and NLP
• Classifying aircraft damage levels and flight phase of safety incidents
• Identifying recurring safety patterns via Topic Modeling in incident reports

Innovation

• AI automates aviation safety report analysis
• NLP classifies aircraft damage and flight phase
• Topic Modeling identifies recurring safety patterns