Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports
Journal:
arXiv
Published Date:
Jan 14, 2025
Abstract
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.
