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Risk Scenario Evaluation for Intelligent Ships by Mapping Hierarchical Holographic Modeling into Risk Filtering, Ranking and Management

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Listed:
  • Wenjun Zhang

    (Navigation College, Dalian Maritime University, Dalian 116026, China)

  • Yingjun Zhang

    (Navigation College, Dalian Maritime University, Dalian 116026, China)

  • Weiliang Qiao

    (Marine Engineering College, Dalian Maritime University, Dalian 116026, China)

Abstract

To identify and screen the risk scenarios for the navigation risk of intelligent ships, the analysis and evaluation of navigational risks were performed in this study. Risk scenarios were developed and evaluated by mapping the hierarchical holographic modeling (HHM) into risk filtering, ranking and management (RFRM). In detail, considering the insignificant influences of some factors on navigational activities, risk factors were filtered and ranked using the RFRM model. Seven final factors were successfully determined, including traffic flow, navigation environment understanding, ship–shore interaction capabilities, target recognition capabilities, communication equipment reliabilities, professional skills, and situation judgments. The results indicated that cargo security can be guaranteed by following navigational risk identification and screening steps, and thus our findings provide theoretical guidance for the dynamic management of maritime organizations and ship companies. In addition, the proposed methodology is desirable for making predictions on maritime traffic risks.

Suggested Citation

  • Wenjun Zhang & Yingjun Zhang & Weiliang Qiao, 2022. "Risk Scenario Evaluation for Intelligent Ships by Mapping Hierarchical Holographic Modeling into Risk Filtering, Ranking and Management," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2103-:d:747869
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Wenjun & Zhang, Yingjun & Zhang, Chuang, 2024. "Research on risk assessment of maritime autonomous surface ships based on catastrophe theory," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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