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Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning

机译：使用事故前兆和近似推理进行重大事故（灰天鹅）可能性建模

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Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well-established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents' relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor-based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.
机译：与普通事故风险分析的显着进步相比，重大事故的风险分析尚未建立完善，部分原因是此类事故的复杂性，部分原因是涉及的概率较低。概率较低的问题通常是由于重大事故的相关数据稀少而引起的，因为此类事故很少且相差甚远。在这项工作中，了解到重大事故往往先于事故前兆，因此，基于事故前兆数据，信息理论和近似推理的独特组合，开发了一种基于前兆的新颖方法，用于关键基础设施中重大事故的可能性建模。为此，我们引入了信息分析的创新应用程序，以识别重大事故中信息最丰富的附近事故。然后将近事故的观测数据用于建立预测情景，以预见重大事故的发生。我们使用墨西哥湾的海上井喷验证了该方法，然后证明了其在美国大坝突破中的应用。

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