HFACS-based Bayesian Network: Machine learning approach to Human Factors in Hydrogen accidents

|Publish Date: May 31, 2024|Keywords: Aviation, Contemporary EHF 2024, Human factors integration, Incidents, Publications|

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 Author
Edem Yao Tsei, David Barry & Duncan Hewat
Abstract
This study combines Bayesian Network (BN) machine learning tool and HFACS to analyse safety risks related to human and organisational factors in hydrogen (H2) accidents in the H2tools database to deduce lessons for aviation. The study statistically identifies significant causal associations between human risk factors and their effect on H2 accidents. Ultimately, the research contributes to the existing human factors knowledge gap in understanding H2 accident risk factors and develops a model for proactive H2 safety management in the aviation domain.