Towards a probabilistic model for predicting ship besetting in ice in Arctic waters
Towards a probabilistic model for predicting ship besetting in ice in Arctic waters
复制标题
建立预测北极水域冰困船舶的概率模型
DOI：
10.1016/j.ress.2016.06.010
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发表时间：
2016-11
影响因子：
8.1
通讯作者：
Zio Enrico
中科院分区：
文献类型：
--
作者：
Fu Shanshan;Zhang Di;Montewka Jakub;Yan Xinping;Zio Enrico
最近，全球变暖导致的海冰融化使商船在北极水域航行成为可能。然而，北极海洋运输系统仍然是一个要求非常高、动态和复杂的系统，因为具有挑战性的水文气象条件、水域制图不佳以及该地区地处偏远，导致在紧急情况下缺乏适当的反应能力。为了确保区域内船舶过境等作业的适当安全水平，应进行风险分析，其中定义了与特定作业有关的因素并将其组织在模型中。这种模式可以帮助陆上管理人员或船员规划和实施通过北极水域的实际海上航道。然而，这一领域的研究很少，主要是因为缺乏数据。在本文中，我们演示了使用数据集和专家判断来确定风险影响因素，并建立了东北航道冰封船舶的概率模型。为此，我们采用贝叶斯信念网络(BBN)，因为它们在不确定情况下的推理能力和适应来自不同来源的数据的能力。得到的BBN模型已得到验证，与现有的最先进模型吻合良好，并提供了对所分析现象的良好理解。
Recently, the melting of sea ice due to global warming has made it possible for merchant ships to navigate through Arctic Waters. However, Arctic Marine Transportation System remains a very demanding, dynamic and complex system due to challenging hydro-meteorological conditions, poorly charted waters and remoteness of the area resulting in lack of appropriate response capacity in case of emergency. In order to ensure a proper safety level for operations such as ship transit within the area, a risk analysis should be carried out, where the relevant factors pertaining to a given operation are defined and organized in a model. Such a model can assist onshore managers or ships’ crews in planning and conducting an actual sea passage through Arctic waters. However, research in this domain is scarce, mainly due to lack of data. In this paper, we demonstrate the use of a dataset and expert judgment to determine the risk influencing factors and develop a probabilistic model for a ship besetting in ice along the Northeast Passage. For that purpose, we adopt Bayesian belief Networks (BBNs), due to their predominant feature of reasoning under uncertainty and their ability to accommodate data from various sources. The obtained BBN model has been validated showing good agreement with available state-of-the-art models, and providing good understanding of the analyzed phenomena.
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影响因子：
3.8
作者：
A. G. Eleye-Datubo;A. Wall;A. Saajedi;Jin Wang
通讯作者：
A. G. Eleye-Datubo;A. Wall;A. Saajedi;Jin Wang
DOI：
10.1177/1748006x11428107
发表时间：
2012-01
期刊：
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
影响因子：
--
作者：
K. Groth;A. Mosleh
通讯作者：
K. Groth;A. Mosleh
影响因子：
7.8
作者：
E. Zio;T. Aven
通讯作者：
E. Zio;T. Aven
影响因子：
9
作者：
E. Zio;E. Zio;T. Aven
通讯作者：
E. Zio;E. Zio;T. Aven
DOI：
--
发表时间：
2013
期刊：
--
影响因子：
--
作者：
J. Montewka;H. Sinclair;P. Kujala;J. Haapala;M. Lensu
通讯作者：
J. Montewka;H. Sinclair;P. Kujala;J. Haapala;M. Lensu