Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier

IF 0.8 Q3 ENGINEERING, MARINE Transactions on Maritime Science-ToMS Pub Date : 2021-10-21 DOI:10.7225/toms.v10.n02.w07
Ana Kuzmanić Skelin, Lea Vojković, Dani Mohović, D. Zec
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引用次数: 1

Abstract

Probabilistic maritime accident models based on Bayesian Networks are typically built upon the data available in accident records and the data obtained from human experts knowledge on accident. The drawback of such models is that they do not take explicitly into the account the knowledge on non-accidents as would be required in the probabilistic modelling of rare events. Consequently, these models have difficulties with delivering interpretation of influence of risk factors and providing sufficient confidence in the risk assessment scores. In this work, modelling and risk score interpretation, as two aspects of the probabilistic approach to complex maritime system risk assessment, are addressed. First, the maritime accident modelling is posed as a classification problem and the Bayesian network classifier that discriminates between accident and non-accident is developed which assesses state spaces of influence factors as the input features of the classifier. Maritime accident risk are identified as adversely influencing factors that contribute to the accident. Next, the weight of evidence approach to reasoning with Bayesian network classifier is developed for an objective quantitative estimation of the strength of factor influence, and a weighted strength of evidence is introduced. Qualitative interpretation of strength of evidence for individual accident influencing factor, inspired by Bayes factor, is defined. The efficiency of the developed approach is demonstrated within the context of collision of small passenger vessels and the results of collision risk assessments are given for the environmental settings typical in Croatian nautical tourism. According to the results obtained, recommendations for navigation safety during high density traffic have been distilled.
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基于贝叶斯网络分类器的海事事故风险评估证据权方法
基于贝叶斯网络的概率海上事故模型通常是建立在事故记录中可用的数据和从人类专家的事故知识中获得的数据之上的。这种模型的缺点是,它们没有明确地考虑到在罕见事件的概率建模中所需要的关于非事故的知识。因此,这些模型在解释风险因素的影响和对风险评估分数提供足够的信心方面存在困难。在这项工作中,建模和风险评分解释,作为复杂海事系统风险评估的概率方法的两个方面,得到了解决。首先,将海事事故建模作为分类问题,开发了区分事故和非事故的贝叶斯网络分类器,将影响因素的状态空间作为分类器的输入特征。海上事故风险被认为是导致事故发生的不利影响因素。其次,提出了利用贝叶斯网络分类器进行推理的证据权重方法,对因素影响强度进行客观定量的估计,并引入了加权证据强度。定义了受贝叶斯因子启发的单个事故影响因素证据强度的定性解释。在小型客船碰撞的背景下证明了开发方法的效率,碰撞风险评估的结果给出了克罗地亚航海旅游典型的环境设置。根据所得结果,对高密度交通条件下的航行安全提出了建议。
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来源期刊
CiteScore
1.50
自引率
12.50%
发文量
0
期刊介绍: ToMS is a scientific journal with international peer review which publishes papers in the following areas: ~ Marine Engineering, ~ Navigation, ~ Safety Systems, ~ Marine Ecology, ~ Marine Fisheries, ~ Hydrography, ~ Marine Automation and Electronics, ~ Transportation and Modes of Transport, ~ Marine Information Systems, ~ Maritime Law, ~ Management of Marine Systems, ~ Marine Finance, ~ Bleeding-Edge Technologies, ~ Multimodal Transport, ~ Psycho-social and Legal Aspects of Long-term Working Aboard. The journal is published in English as an open access journal, and as a classic paper journal (in limited editions). ToMS aims to present best maritime research from South East Europe, particularly the Mediterranean area. Articles will be double-blind reviewed by three reviewers. With the intention of providing an international perspective at least one of the reviewers will be from abroad. ToMS also promotes scientific collaboration with students and has a section titled Students’ ToMS. These papers also undergo strict peer reviews. Furthermore, the Journal publishes short reviews on significant papers, books and workshops in the fields of maritime science.
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