Aviation safety is paramount, demanding precise analysis of safety occurrences during different flight phases. This study employs Natural Language Processing (NLP) and Deep Learning models, including LSTM, CNN, Bidirectional LSTM (BLSTM), and simple Recurrent Neural Networks (sRNN), to classify flight phases in safety reports from the Australian Transport Safety Bureau (ATSB). The models exhibited high accuracy, precision, recall, and F1 scores, with LSTM achieving the highest performance of 87%, 88%, 87%, and 88%, respectively. This performance highlights their effectiveness in automating safety occurrence analysis. The integration of NLP and Deep Learning technologies promises transformative enhancements in aviation safety analysis, enabling targeted safety measures and streamlined report handling.
@article{arxiv.2501.07923,
title = {Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports},
author = {Aziida Nanyonga and Hassan Wasswa and Graham Wild},
journal= {arXiv preprint arXiv:2501.07923},
year = {2025}
}
Comments
NLP, Aviation Safety, ATSB, Deep learning, Flight phase. arXiv admin note: substantial text overlap with arXiv:2501.01694