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Accident analysis and risk prediction of tank farm based on Bayesian network method

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Listed:
  • Xingguang Wu
  • Huirong Huang
  • Weichao Yu
  • Yuming Lin
  • Yanhui Xue
  • Qingwen Cai
  • Jili Xu

Abstract

In recent decades, many attempts have been made to establish the cause-effect relationship model of accidents, while little work has been carried out to comprehensively consider the interdependence between the causal factors and their complex interactions with the accident outcomes. In this study, a novel accident analysis approach based on Bayesian networks (BNs) was proposed to achieve quantitative accident analysis and dynamic risk prediction of accident types and consequences. To develop the BN-based accident analysis model, a total of 1144 accident cases occurred in tank farm of China from 1960 to 2018 were collected. The BN model that can comprehensively characterize the dependencies among accident elements was established through structural learning based on accident case analysis and parameter learning based on EM algorithm. The reliability and validity of the BN model were verified by k -fold cross-validation method and comparison of predicted data with real data, and the results showed that the BN model had good classification and prediction performance. Furthermore, the established BN model was applied to the accident occurred in Huangdao, China. The analysis results show that not only the accident outcome can be accurately predicted, but also the hidden correlation can be deeply explored through the established BN model. The proposed method and findings can provide technical reference for accident investigation and analysis, and provide decision support for accident prevention and risk management.

Suggested Citation

  • Xingguang Wu & Huirong Huang & Weichao Yu & Yuming Lin & Yanhui Xue & Qingwen Cai & Jili Xu, 2024. "Accident analysis and risk prediction of tank farm based on Bayesian network method," Journal of Risk and Reliability, , vol. 238(2), pages 366-386, April.
    • Handle: RePEc:sae:risrel:v:238:y:2024:i:2:p:366-386
    DOI: 10.1177/1748006X221139906
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    References listed on IDEAS

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