Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia
This study investigates the application of advanced deep learning models for the classification of aviation safety incidents, focusing on four models: Simple Recurrent Neural Network (sRNN), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BLSTM), and DistilBERT. The models were eva...
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MDPI AG
2025-05-01
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| Series: | Modelling |
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| Online Access: | https://www.mdpi.com/2673-3951/6/2/40 |
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| author | Aziida Nanyonga Keith Joiner Ugur Turhan Graham Wild |
| author_facet | Aziida Nanyonga Keith Joiner Ugur Turhan Graham Wild |
| author_sort | Aziida Nanyonga |
| collection | DOAJ |
| description | This study investigates the application of advanced deep learning models for the classification of aviation safety incidents, focusing on four models: Simple Recurrent Neural Network (sRNN), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BLSTM), and DistilBERT. The models were evaluated based on key performance metrics, including accuracy, precision, recall, and F1-score. DistilBERT achieved perfect performance with an accuracy of 1.00 across all metrics, while BLSTM demonstrated the highest performance among the deep learning models, with an accuracy of 0.9896, followed by GRU (0.9893) and sRNN (0.9887). Class-wise evaluations revealed that DistilBERT excelled across all injury categories, with BLSTM outperforming the other deep learning models, particularly in detecting fatal injuries, achieving a precision of 0.8684 and an F1-score of 0.7952. The study also addressed the challenges of class imbalance by applying class weighting, although the use of more sophisticated techniques, such as focal loss, is recommended for future work. This research highlights the potential of transformer-based models for aviation safety classification and provides a foundation for future research to improve model interpretability and generalizability across diverse datasets. These findings contribute to the growing body of research on applying deep learning techniques to aviation safety and underscore opportunities for further exploration. |
| format | Article |
| id | doaj-art-38599fbf517f4487b2b2c09e4ecabb3d |
| institution | Kabale University |
| issn | 2673-3951 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Modelling |
| spelling | doaj-art-38599fbf517f4487b2b2c09e4ecabb3d2025-08-20T03:29:44ZengMDPI AGModelling2673-39512025-05-01624010.3390/modelling6020040Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in AustraliaAziida Nanyonga0Keith Joiner1Ugur Turhan2Graham Wild3School of Engineering and Technology, University of New South Wales, Canberra, ACT 2600, AustraliaCapability Systems Centre, University of New South Wales, Canberra, ACT 2610, AustraliaSchool of Science, University of New South Wales, Canberra, ACT 2612, AustraliaSchool of Science, University of New South Wales, Canberra, ACT 2612, AustraliaThis study investigates the application of advanced deep learning models for the classification of aviation safety incidents, focusing on four models: Simple Recurrent Neural Network (sRNN), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BLSTM), and DistilBERT. The models were evaluated based on key performance metrics, including accuracy, precision, recall, and F1-score. DistilBERT achieved perfect performance with an accuracy of 1.00 across all metrics, while BLSTM demonstrated the highest performance among the deep learning models, with an accuracy of 0.9896, followed by GRU (0.9893) and sRNN (0.9887). Class-wise evaluations revealed that DistilBERT excelled across all injury categories, with BLSTM outperforming the other deep learning models, particularly in detecting fatal injuries, achieving a precision of 0.8684 and an F1-score of 0.7952. The study also addressed the challenges of class imbalance by applying class weighting, although the use of more sophisticated techniques, such as focal loss, is recommended for future work. This research highlights the potential of transformer-based models for aviation safety classification and provides a foundation for future research to improve model interpretability and generalizability across diverse datasets. These findings contribute to the growing body of research on applying deep learning techniques to aviation safety and underscore opportunities for further exploration.https://www.mdpi.com/2673-3951/6/2/40natural language processingaviation safetydistilled BERTsRNN |
| spellingShingle | Aziida Nanyonga Keith Joiner Ugur Turhan Graham Wild Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia Modelling natural language processing aviation safety distilled BERT sRNN |
| title | Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia |
| title_full | Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia |
| title_fullStr | Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia |
| title_full_unstemmed | Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia |
| title_short | Natural Language Processing for Aviation Safety: Predicting Injury Levels from Incident Reports in Australia |
| title_sort | natural language processing for aviation safety predicting injury levels from incident reports in australia |
| topic | natural language processing aviation safety distilled BERT sRNN |
| url | https://www.mdpi.com/2673-3951/6/2/40 |
| work_keys_str_mv | AT aziidananyonga naturallanguageprocessingforaviationsafetypredictinginjurylevelsfromincidentreportsinaustralia AT keithjoiner naturallanguageprocessingforaviationsafetypredictinginjurylevelsfromincidentreportsinaustralia AT ugurturhan naturallanguageprocessingforaviationsafetypredictinginjurylevelsfromincidentreportsinaustralia AT grahamwild naturallanguageprocessingforaviationsafetypredictinginjurylevelsfromincidentreportsinaustralia |