Analysis of human risks due to dam-break floods-part 1: a new model based on Bayesian networks
Analysis of human risks due to dam-break floods-part 1: a new model based on Bayesian networks
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DOI：
10.1007/s11069-012-0275-5
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发表时间：
2012-10-01
期刊：
影响因子：
3.7
通讯作者：
Zhang, L. M.
中科院分区：
文献类型：
--
作者：
Peng, M.;Zhang, L. M.
溃坝对人类生命造成灾难性后果。本文提出了一种新的基于贝叶斯网络的人类风险分析模型(HURAM)，用于估计溃坝洪水的人类风险。根据生命损失机制的逻辑结构，构建了贝叶斯网络。利用历史数据、现有模型和物理分析来量化网络的节点(参数)和弧线(相互关系)。为此目的编制了343例溃坝案例和死亡记录的数据集。将现有的两种模型与新模型进行了比较，验证了新模型的有效性。最后，进行灵敏度分析，找出导致生命损失的重要参数。新模型能够在一个系统结构中考虑到大量重要参数及其相互关系；包括这些参数的不确定性及其相互关系；纳入物理分析、经验模型和历史数据得出的信息；并在有具体案例的信息时更新预测。这一模型在一个具体的溃坝案例中的人的风险研究中的应用在一篇配文中给出。
Dam breaks have catastrophic consequences for human lives. This paper presents a new human risk analysis model (HURAM) using Bayesian networks for estimating human risks due to dam-break floods. A Bayesian network is constructed according to a logic structure of loss-of-life mechanisms. The nodes (parameters) and the arcs (inter-relationships) of the network are quantified with historical data, existing models and physical analyses. A dataset of 343 dam-failure cases with records of fatality is compiled for this purpose. Comparison between two existing models and the new model is made to test the new model. Finally, sensitivity analysis is conducted to identify the important parameters that lead to loss of life. The new model is able to take into account a large number of important parameters and their inter-relationships in a systematic structure; include the uncertainties of these parameters and their inter-relationships; incorporate information derived from physical analysis, empirical models and historical data; and update the predictions when information in specific cases is available. The application of this model to the study of human risks in a specific dam-break case is presented in a companion paper.