加强海运安全:对恐怖袭击风险的数据驱动贝叶斯网络分析
Massoud Mohsendokht1, Huanhuan Li1, Christos Kontovas1
1Faculty of Engineering and Technology, Liverpool Logistics, Offshore and Marine (LOOM) Research Institute, Liverpool John Moores University, Liverpool, Merseyside, UK.
概括
海上恐怖主义带来了低频率,高后果的风险. 本研究介绍了一种数据驱动的贝叶斯网络 (DDBN) 模型,使用事故数据来分析海上安全风险,并确定导致的因素.
科学领域:
- 海上安全 海上安全
- 风险分析 风险分析
- 恐怖主义研究 研究 恐怖主义研究
背景情况:
- 海上恐怖主义事故很少发生,但严重,需要新的分析方法.
- 现有的关于海上恐怖主义风险的研究是有限的,突出了关键的知识差距.
- 了解海上安全威胁中的不确定性和相互依存性至关重要.
研究的目的:
- 开发和验证用于海上安全风险分析的新型数据驱动贝叶斯网络 (DDBN) 模型.
- 确定导致海上恐怖事件的关键因素及其相互关系.
- 评估各种海上恐怖主义情景的可能性和影响.
主要方法:
- 利用过去二十年的历史海上恐怖袭击事故数据.
- 开发了一个数据驱动的贝叶斯网络 (DDBN) 模型用于风险评估.
- 用于模型验证和验证的敏感度,指标和比较分析.
- 测试模型与最近的现实世界案例进行回顾性和前性分析.
主要成果:
- DDBN模型有效地识别了关键的贡献因素及其复杂的相互依赖关系.
- 确定了不同恐怖情景的可能性及其对海上作战的影响.
- 该模型在风险传播方面表现出强大的诊断和预测能力.
- 对现实事件的验证证实了该模型的有效性.
结论:
- 开发的DDBN模型为了解和管理海上恐怖主义风险提供了一个强大的框架.
- 调查结果为公司和政府机构提供了可操作的见解,以加强海上安全.
- 这项研究有助于加强防范措施和应急管理战略,打击海上恐怖主义.
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