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A quantitative model for estimating risk from multiple interacting natural hazards: an application to northeast Zhejiang, China

机译:一种估算多种相互作用自然灾害风险的定量模型:适用于中国浙江东北部

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摘要

Multi-hazard risk assessment is a major concern in risk analysis, but most approaches do not consider all hazard interactions when calculating possible losses. We address this problem by developing an improved quantitative model - Model for multi-hazard Risk assessment with a consideration of Hazard Interaction (MmhRisk-HI). This model calculates the possible loss caused by multiple hazards, with an explicit consideration of interaction between those hazards. There are two main components to the model. In the first, based on the hazard-forming environment, relationships among hazards are classified into four types for calculation of the exceedance probability of multiple hazards occurrence. In the second, a Bayesian network is used to calculate possible loss caused by multiple hazards with different exceedance probabilities. A multi-hazard risk map can then be drawn addressing the probability of multi-hazard occurrence and corresponding loss. This model was applied in northeast Zhejiang, China and validated by comparison against an observed multi-hazard sequence. The validation results show that the model can more effectively represent the real world, and that the modelled outputs, possible loss caused by multiple hazards, are reliable. The outputs can additionally help to identify areas at greatest risk, and allows a determination of the factors that contribute to that risk, and hence the model can provide useful further information for planners and decision-makers concerned with risk mitigation.
机译:多危害风险评估是风险分析中的主要问题,但是大多数方法在计算可能的损失时并未考虑所有危害相互作用。我们通过开发改进的定量模型-考虑危害相互作用(MmhRisk-HI)的多危害风险评估模型来解决此问题。该模型通过明确考虑这些危害之间的相互作用来计算由多种危害导致的可能损失。该模型有两个主要组成部分。首先,根据危险形成环境,将危险之间的关系分为四种类型,以计算多重危险发生的超标概率。在第二种方法中,贝叶斯网络用于计算由具有不同超标概率的多种危害导致的可能损失。然后可以绘制多危害风险图,以解决多危害发生的概率和相应的损失。该模型在中国浙江东北部应用,并与观察到的多灾种序列进行了比较验证。验证结果表明,该模型可以更有效地表示现实世界,并且模型输出(由多种危害引起的可能损失)是可靠的。输出还可以帮助识别风险最大的区域,并确定导致该风险的因素,因此该模型可以为与减轻风险相关的计划者和决策者提供有用的进一步信息。

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  • 作者

    Liu B; Siu YL; Mitchell G;

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  • 年度 2017
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  • 原文格式 PDF
  • 正文语种 en
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