With the acceleration of global climate change and urbanization, disaster chains are always connected to artificial systems like critical infrastructure. The complexity and uncertainty of the disaster chain development process and the severity of the consequences have brought great challenges to emergency decision makers. The Bayesian network (BN) was applied in this study to reason about disaster chain scenarios to support the choice of appropriate response strategies. To capture the interacting relationships among different factors, a scenario representation model of disaster chains was developed, followed by the determination of the BN structure. In deriving the conditional probability tables of the BN model, we found that, due to the lack of data and the significant uncertainty of disaster chains, parameter learning methodologies based on data or expert knowledge alone are insufficient. By integrating both sample data and expert knowledge with the maximum entropy principle, we proposed a parameter estimation algorithm under expert prior knowledge (PEUK). Taking the rainstorm disaster chain as an example, we demonstrated the superiority of the PEUK-built BN model over the traditional maximum a posterior (MAP) algorithm and the direct expert opinion elicitation method. The results also demonstrate the potential of our BN scenario reasoning paradigm to assist real-world disaster decisions.

随着全球气候变化和城市化的加速，灾害链总是与关键基础设施等人工系统相连。灾害链发展过程的复杂性和不确定性以及后果的严重性给应急决策者带来了巨大挑战。本研究应用贝叶斯网络（BN）来推理灾难链情景，以支持选择适当的应对策略。为了捕捉不同因素之间的相互作用关系，开发了灾害链的情景表示模型，然后确定了 BN 结构。在推导BN模型的条件概率表时，我们发现，由于数据的缺乏以及灾害链的巨大不确定性，仅基于数据或专家知识的参数学习方法是不够的。通过将样本数据和专家知识结合最大熵原理，提出了专家先验知识（PEUK）下的参数估计算法。以暴雨灾害链为例，证明了PEUK构建的BN模型相对于传统最大后验（MAP）算法和直接专家意见获取方法的优越性。结果还证明了我们的 BN 场景推理范式在协助现实世界灾难决策方面的潜力。