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Assessment of aviation accident datasets in severity prediction through machine learning

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  • Omrani, Farzane
  • Etemadfard, Hossein
  • Shad, Rouzbeh

Abstract

The importance and seriousness of civil aviation safety have become more noticeable due to the intricate advancement of air transportation. It is crucial to apply diverse datasets to evaluate and anticipate the degree of aviation safety. Data on the different causes of accidents can be analyzed and applied to predict and prevent potential accidents. The main goal of this research is to explore how Machine Learning (ML) techniques can be used to identify correlations and connections between various contributing factors in aviation accidents. It applies ML algorithms on datasets extracted from original data sourced from the National Transportation Safety Board (NTSB) of the United States, Transportation Safety Board (TSB) of Canada, and Australian Transport Safety Bureau (ATSB) aviation accident records spanning from January 2008 to January 2023. Initially, this study uses Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM) models to predict the civil aviation accident rates for the United States, Canada, and Australia data separately. Sequentially, by extracting Common Features (CF) and reclassifying the Common Sub-Features (CSF) of these three countries, a comprehensive model is created. Finally, it evaluates the differences and limitations of these countries' datasets. The results show that the separate models exhibit a higher level of prediction accuracy and lower errors compared to the common models and the best results (81% accuracy) were achieved through SVM.

Suggested Citation

  • Omrani, Farzane & Etemadfard, Hossein & Shad, Rouzbeh, 2024. "Assessment of aviation accident datasets in severity prediction through machine learning," Journal of Air Transport Management, Elsevier, vol. 115(C).
  • Handle: RePEc:eee:jaitra:v:115:y:2024:i:c:s0969699723001746
    DOI: 10.1016/j.jairtraman.2023.102531
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    References listed on IDEAS

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    1. Rao, Arjun H. & Marais, Karen, 2018. "High risk occurrence chains in helicopter accidents," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 83-98.
    2. Olja Čokorilo & Mario De Luca & Gianluca Dell’Acqua, 2014. "Aircraft safety analysis using clustering algorithms," Journal of Risk Research, Taylor & Francis Journals, vol. 17(10), pages 1325-1340, November.
    3. Tüzün Tolga İnan & Neslihan Gökmen İnan, 2022. "The analysis of fatal aviation accidents more than 100 dead passengers: an application of machine learning," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1377-1395, December.
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