贝叶斯网络
过程(计算)
弹性(材料科学)
计算机科学
风险分析(工程)
贝叶斯推理
鉴定(生物学)
运筹学
工程类
贝叶斯概率
人工智能
业务
物理
植物
生物
热力学
操作系统
作者
Yang Liu,Xiaoxue Ma,Weiliang Qiao,Laihao Ma,Bing Han
标识
DOI:10.1016/j.ress.2023.109620
摘要
Disruption cognition is critical for the development of resilient maritime transportation systems to withstand uncertain risks and achieve sustainable development. Aiming at improving the resilience of maritime transportation systems, a comprehensive methodology is proposed in the present study to model the propagation process of disruptions. First, a conceptual framework of disruption propagation within resilience theory is developed for the maritime transportation system, based on which a directed weighted complex network of disruption propagation is established, and data-driven Bayesian inference is applied to extend the complex network using a probability-based method. The propagation process and mechanisms can then be analysed quantitatively through critical node identification for each propagation stage and the determination of the shortest propagative paths by the combination of bidirectional Bayesian inference, sensitivity analysis, and uncertainty analysis. Then, the proposed methodology is applied to the Arctic maritime transportation system to improve resilience by controlling the key disruptions in each propagation stage and cutting off the critical disruption propagation paths. The findings suggest that greater effort should be devoted to strengthening the resilience aspects related to environmental forecast and route planning systems, monitoring and functional maintenance mechanisms, emergency responses pertaining to repair and damage control, emergency escape and evacuation, and coastal SAR services to reduce the escalated impact of disruption propagation.
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