4.6 Article

A scenario modelling method for regional cascading disaster risk to support emergency decision making

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ELSEVIER
DOI: 10.1016/j.ijdrr.2022.103102

关键词

Cascading disasters; Scenario modelling; Disaster evolution; Elements at risk; Topological interrelation of impact

资金

  1. National Natural Science Foundation of China [71871039]
  2. MOE Project of Humanities and Social Sciences [19YJC630130]

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This study proposes a scenario modelling method for cascading disasters to support decision making in complex disaster emergency preparation and response. The method considers the interconnections of different events and elements at risk in disaster-affected regions, and utilizes a conceptual framework and dynamic evolution model to analyze and identify critical disaster scenarios and cascading risks.
A scenario modelling method for cascading disasters was proposed in this study to support decision making for complex disaster emergency preparation and response. Considering the causality of different events and complex interconnections of the elements at risk in disaster-affected regions, we constructed a conceptual framework for regional cascading disaster scenario analysis based on the regional disaster system theory. Subsequently, an interrelation network generation method to depict a hazard-affected region was proposed considering the characteristics of the disaster system; consequently, a dynamic evolution model of cascading disasters was developed. Further, a virtual region was designed to illustrate the application of the region modelling method and the dynamic evolution model of cascading disasters. Finally, we proposed two dimensions for analysis of cascading risk based on the scenario simulation results: the disaster consequence evolution trend of typical elements at risk and the disaster consequence cascading risk from the perspective of network structures. They can help identify the critical disaster scenario slices that aggravate the cascading effect of disaster consequence and be referenced to make efficient emergency decisions under cascading disaster scenarios.

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