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多灾害情景生成器:自然灾害的网络仿真

Multihazards Scenario Generator: A Network-Based Simulation of Natural Disasters.

机构信息

Department of Geological Sciences, University of Canterbury, Christchurch, New Zealand.

Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand.

出版信息

Risk Anal. 2021 Nov;41(11):2154-2176. doi: 10.1111/risa.13723. Epub 2021 Mar 17.

Abstract

The impact of natural disasters has been increasing in recent years. Despite the developing international interest in multihazard events, few studies quantify the dynamic interactions that characterize these phenomena. It is argued that without considering the dynamic complexity of natural catastrophes, impact assessments will underestimate risk and misinform emergency management priorities. The ability to generate multihazard scenarios with impacts at a desired level is important for emergency planning and resilience assessment. This article demonstrates a framework for using graph theory and networks to generate and model the complex impacts of multihazard scenarios. First, the combination of maximal hazard footprints and exposed nodes (e.g., infrastructure) is used to create the hazard network. Iterative simulation of the network, defined by actual hazard magnitudes, is then used to provide the overall compounded impact from a sequence of hazards. Outputs of the method are used to study distributional ranges of multihazards impact. The 2016 Kaikōura earthquake is used as a calibrating event to demonstrate that the method can reproduce the same scale of impacts as a real event. The cascading hazards included numerous landslides, allowing us to show that the scenario set generated includes the actual impacts that occurred during the 2016 events.

摘要

近年来,自然灾害的影响一直在加剧。尽管国际上对多灾害事件的兴趣日益浓厚,但很少有研究能够量化这些现象的动态相互作用。有人认为,如果不考虑自然灾害的动态复杂性,那么影响评估将低估风险,并错误地指导应急管理的优先事项。能够生成具有所需影响水平的多灾害情景的能力对于应急规划和弹性评估非常重要。本文展示了一种使用图论和网络生成和模拟多灾害情景复杂影响的框架。首先,将最大灾害足迹和暴露节点(例如基础设施)组合起来,以创建灾害网络。然后,通过实际灾害规模来定义网络的迭代模拟,从而提供一系列灾害的总体复合影响。该方法的输出用于研究多灾害影响的分布范围。2016 年凯库拉地震被用作校准事件,以证明该方法可以复制真实事件的相同规模的影响。所包含的级联灾害有许多滑坡,这使我们能够表明生成的情景集包含了 2016 年事件中实际发生的影响。

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