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A Digital Template for the Generic Multi-Risk (GenMR) Framework: A Virtual Natural Environment

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  • Arnaud Mignan

    (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)

Abstract

Extreme disasters, defined as low-probability–high-consequences events, are often due to cascading effects combined to amplifying environmental factors. While such a risk complexity is commonly addressed by the modeling of site-specific multi-risk scenarios, there exists no harmonized approach that considers the full space of possibilities, based on the general relationships between the environment and the perils that populate it. In this article, I define the concept of a digital template for multi-risk R&D and prototyping in the Generic Multi-Risk (GenMR) framework. This digital template consists of a virtual natural environment where different perils may occur. They are geological (earthquakes, landslides, volcanic eruptions), hydrological (river floods, storm surges), meteorological (windstorms, heavy rains), and extraterrestrial (asteroid impacts). Both geological and hydrological perils depend on the characteristics of the natural environment, here defined by two environmental layers : topography and soil. Environmental objects , which alter the layers, are also defined. They are here geomorphic structures linked to some peril source characteristics. Hazard intensity footprints are then generated for primary, secondary, and tertiary perils. The role of the natural environment on intensity footprints and event cascading is emphasized, one example being the generation of a “quake lake”. Future developments, à la SimCity , are finally discussed.

Suggested Citation

  • Arnaud Mignan, 2022. "A Digital Template for the Generic Multi-Risk (GenMR) Framework: A Virtual Natural Environment," IJERPH, MDPI, vol. 19(23), pages 1-22, December.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16097-:d:990757
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    References listed on IDEAS

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    5. Arnaud Mignan & Matteo Spada & Peter Burgherr & Ziqi Wang & Didier Sornette, 2022. "Dynamics of severe accidents in the oil & gas energy sector derived from the authoritative ENergy-related severe accident database," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-14, February.
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