基于贝叶斯网络的船舶事故环境风险预测

I. Koromila, Z. Nivolianitou, Theodoros Giannakopoulos
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引用次数: 22

摘要

AMINESS项目的目标是通过门户网站促进爱琴海的航运安全,为船东、政策制定者、科学界和公众等相关利益攸关方提供不同层次的访问。门户将有三个主要用途。一是提出船舶和环境最优安全航路规划。二是根据船舶的位置和计划航线、货物和气象/海况,实时为船舶发出与其他船舶有关的潜在危险警报。最后,第三是通过分析与船舶轨迹相关的短期和长期历史数据来支持政策建议。为此,正在使用贝叶斯网络(BN)计算爱琴海可能发生事故的风险。研究了两种事故情景(碰撞和接地)。一个简化的贝叶斯模型已经发展到预测事故的风险给定船舶的主要特征,即船型,大小,年龄和旗帜,这是输入到目前的模型。适当的输入数据已由AIS(自动识别系统)签署我们。利用海上救援协调中心的历史事故数据库和AIS系统的数据对所开发的贝叶斯网络进行了训练,并给出了爱琴海地区的一些用例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian network to predict environmental risk of a possible ship accident
The goal of the AMINESS project is to promote shipping safety in the Aegean Sea though a web portal offering different levels of access to relevant stakeholders such as ship owners, policy makers, the scientific community and the general public. The portal will have three principle uses. The first is to suggest both vessel and environmentally optimal safe route planning for ships. The second is to produce alerts for ships in real time with respect to potential hazards associated to other ships, as a function of its location and planned route, its cargo and the meteorological/sea conditions. Finally, the third is to support policy recommendations, through analysis of historical data in short and long term periods that correlate safety with ship trajectories. To that end, the risk of a possible accident occurrence in the Aegean Sea is being calculated using Bayesian networks (BN). Two types of accident scenarios (collision and grounding) have been studied. A simplified Bayesian model has been developed to predict the risk of an accident given the main characteristics of the vessel, namely the ship type, size, age and flag, which are inputted to the present model. The appropriate input data has been provided by the AIS (Automatic Identification System) sign us with. Training of the developed Bayesian network was performed using the data of both the historical accident database of Marine Rescue Coordination Center and the AIS and some use cases in the area of Aegean Sea is presented in this paper.
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