Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports
Semantic Scholar · Article (2023 Global Conference on Information Technologies and Communications (GCITC)) · 878521a1d44d6e8ee97130a382075cb99e65cd8f · Published 2023-12-01 · 2023 Global Conference on Information Technologies and Communications (GCITC) · 3 authors
Abstract and citation only, verbatim from Semantic Scholar; full text lives there. All credit to the authors and 2023 Global Conference on Information Technologies and Communications (GCITC).
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.
Authors
- Aziida Nanyonga
- Hassan Wasswa
- Graham Wild
Keywords
- Computer Science
- Engineering
Citation
Aziida Nanyonga, Hassan Wasswa, Graham Wild (2023). Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports. 2023 Global Conference on Information Technologies and Communications (GCITC). Semantic Scholar ID 878521a1d44d6e8ee97130a382075cb99e65cd8f. https://doi.org/10.1109/GCITC60406.2023.10426306 ↗