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用灾害动力学探索级联灾害的可能性空间。

Exploring the Space of Possibilities in Cascading Disasters with Catastrophe Dynamics.

机构信息

Institute of Risk Analysis, Prediction and Management (Risks-X), Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China.

Department of Earth and Space Sciences, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China.

出版信息

Int J Environ Res Public Health. 2020 Oct 7;17(19):7317. doi: 10.3390/ijerph17197317.

Abstract

Some of the most devastating natural events on Earth, such as earthquakes and tropical cyclones, are prone to trigger other natural events, critical infrastructure failures, and socioeconomic disruptions. Man-made disasters may have similar effects, although to a lesser degree. We investigate the space of possible interactions between 19 types of loss-generating events, first by encoding possible one-to-one interactions into an adjacency matrix A, and second by calculating the interaction matrix M of emergent chains-of-events. We first present the impact of 24 topologies of A on M to illustrate the non-trivial patterns of cascading processes, in terms of the space of possibilities covered and of interaction amplification by feedback loops. We then encode A from 29 historical cases of cascading disasters and compute the matching matrix M. We observe, subject to data incompleteness, emergent cascading behaviors in the technological and socioeconomic systems, across all possible triggers (natural or man-made); disease is also a systematic emergent phenomenon. We find interactions being mostly amplified via two events: network failure and business interruption, the two events with the highest in-degree and betweenness centralities. This analysis demonstrates how cascading disasters grow in and cross over natural, technological, and socioeconomic systems.

摘要

地球上一些最具破坏性的自然事件,如地震和热带气旋,容易引发其他自然事件、关键基础设施故障和社会经济破坏。人为灾害可能具有类似的影响,尽管程度较轻。我们研究了 19 种致损事件之间可能的相互作用空间,首先将可能的一对一相互作用编码为邻接矩阵 A,其次通过计算突发事件链的交互矩阵 M。我们首先展示 A 的 24 种拓扑结构对 M 的影响,以说明级联过程的非平凡模式,包括所涵盖的可能性空间和反馈循环的交互放大。然后,我们根据 29 个级联灾害的历史案例对 A 进行编码,并计算匹配矩阵 M。我们观察到,受数据不完整性的影响,在所有可能的触发因素(自然或人为)下,技术和社会经济系统中出现了级联行为;疾病也是一种系统性的突发现象。我们发现,通过两个事件放大了相互作用:网络故障和业务中断,这两个事件具有最高的入度和中间中心性。这种分析表明级联灾害如何在自然、技术和社会经济系统中增长和跨越。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/237d/7579666/18962307484a/ijerph-17-07317-g0A1.jpg

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