Realising advanced risk assessment of vessel traffic flows near offshore wind farms
Realising advanced risk assessment of vessel traffic flows near offshore wind farms
复制标题
DOI：
10.1016/j.ress.2020.107086
复制
发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
Q. Yu;Kezhong Liu;Chia-Hsun Chang;Zaili Yang
中科院分区：
文献类型：
--
作者：
Q. Yu;Kezhong Liu;Chia-Hsun Chang;Zaili Yang
海上风电场（OWF）是相对较新的海上设施。与船舶碰撞OWF相关的事故记录不足以支持使用传统概率方法进行全面的定量风险分析。本文旨在开发一个半定性的风险模型来评估船舶涡轮机碰撞风险，结合贝叶斯网络（BN）和证据推理（ER）的方法。首先，基于自动识别系统（AIS）数据训练BN来描述真实的船舶交通流，包括交通流参数之间的详细信息和关系。其次，通过ER综合专家判断，识别出影响船机碰撞概率和后果的5个风险因素（包括：在所建立的BN中的相关联的条件概率）。最后，通过十个真实的场景对ER输入的更新BN进行测试，并通过处理有效性框架进行验证。本文率先使用多数据驱动的贝叶斯网络来预测交通流量和评估船舶涡轮机碰撞风险，以确保OWF附近的航行安全。研究结果提供了经验证据，使用ER补充BN主观数据，以推进其在风险分析中的应用。
Offshore wind farms (OWFs) are relatively new installations at sea. Accident records related to vessel collisions with OWFs are insufficient to support a full quantitative risk analysis using traditional probabilistic approaches. This paper aims to develop a semi-qualitative risk model to assess the vessel-turbine collision risks by incorporating Bayesian networks (BN) with evidential reasoning (ER) approaches. First, a BN is trained based on Automatic Identification Systems (AIS) data to characterise real vessel traffic flows, including the detailed information and relationships between traffic flow parameters. Secondly, through synthesising expert judgements by ER, five risk factors influencing the probability and consequence of vessel-turbine collisions are identified (incl. the associated conditional probabilities) in the established BN. Finally, the updated BN with ER input is tested through ten real scenarios and validated by processing a validity framework. This paper pioneers the use of multi-data-driven BNs to characterise traffic flows and assess vessel-turbine collision risk for navigational safety assurance near OWFs. The research findings provide empirical evidence of using ER to supplement BN subjective data to advance its applications in risk analysis.