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Machine learning for helicopter accident analysis using supervised classification: Inference, prediction, and implications

Author

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  • Xu, Zhaoyi
  • Saleh, Joseph Homer
  • Subagia, Rachmat

Abstract

The work is part of a larger effort whose end-objective is to contribute toward a better understanding of helicopter accidents and improving their safety track record. Herein, we extend the domain of application of Machine Learning (ML) to a new topic, namely helicopter accidents. Our objectives are twofold: (1) to benchmark the performance of different classifiers in examining our dataset, and (2) to leverage the best-in-class classifier to identify novel insights for improving helicopter accident analysis and prevention.

Suggested Citation

  • Xu, Zhaoyi & Saleh, Joseph Homer & Subagia, Rachmat, 2020. "Machine learning for helicopter accident analysis using supervised classification: Inference, prediction, and implications," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020307110
    DOI: 10.1016/j.ress.2020.107210
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    References listed on IDEAS

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    1. Rachmat Subagia & Joseph Homer Saleh & Jared S Churchwell & Katherine S Zhang, 2020. "Statistical learning for turboshaft helicopter accidents using logistic regression," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-21, January.
    2. Churchwell, Jared S. & Zhang, Katherine S. & Saleh, Joseph H., 2018. "Epidemiology of helicopter accidents: Trends, rates, and covariates," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 373-384.
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    Citations

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    Cited by:

    1. Cao, Bohan & Yin, Qishuai & Guo, Yingying & Yang, Jin & Zhang, Laibin & Wang, Zhenquan & Tyagi, Mayank & Sun, Ting & Zhou, Xu, 2023. "Field data analysis and risk assessment of shallow gas hazards based on neural networks during industrial deep-water drilling," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    2. Pan, Yongjun & Sun, Yu & Li, Zhixiong & Gardoni, Paolo, 2023. "Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Ziegler Haselein, Bruno & da Silva, Jonny Carlos & Hooey, Becky L., 2024. "Multiple machine learning modeling on near mid-air collisions: An approach towards probabilistic reasoning," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Rose, Rodrigo L. & Puranik, Tejas G. & Mavris, Dimitri N. & Rao, Arjun H., 2022. "Application of structural topic modeling to aviation safety data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Munim, Ziaul Haque & Sørli, Michael André & Kim, Hyungju & Alon, Ilan, 2024. "Predicting maritime accident risk using Automated Machine Learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    6. Zhang, Hengqi & Geng, Hua, 2023. "A methodology to identify and assess high-risk causes for electrical personal accidents based on directed weighted CN," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. Simsekler, Mecit Can Emre & Rodrigues, Clarence & Qazi, Abroon & Ellahham, Samer & Ozonoff, Al, 2021. "A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 208(C).

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