Reasoning Disaster Chains with Bayesian Network Estimated Under Expert Prior Knowledge  

作　　者：Lida Huang Tao Chen Qing Deng Yuli Zhou 

机构地区：[1]Institute of Public Safety Research,Department of Engineering Physics,Tsinghua University,Beijing 100084,China [2]School of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing 100083,China [3]Institute of National Security and Development Strategic Studies,Beijing Normal University,Beijing 100875,China

出　　处：《International Journal of Disaster Risk Science》2023年第6期1011-1028,共18页国际灾害风险科学学报（英文版）

基　　金：supported by the National Key Research and Development Program of China(Grant No.2021YFF0600400);the National Natural Science Foundation of China(Grant Nos.72104123,72004113)。

摘　　要：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.

关 键 词：Bayesian network Expert prior knowledge Parameter learning Rainstorm disaster chain Scenario reasoning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] X4[自动化与计算机技术—控制科学与工程]