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通用多风险(GenMR)框架的数字模板:虚拟自然环境。

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

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. 2022 Dec 1;19(23):16097. doi: 10.3390/ijerph192316097.

DOI:10.3390/ijerph192316097
PMID:36498170
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9736322/
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 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 : topography and soil. , 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 , are finally discussed.

摘要

极端灾害是指低概率高后果事件,通常是由级联效应与放大的环境因素共同导致的。虽然这种风险复杂性通常可以通过针对特定地点的多风险情景进行建模来解决,但目前还没有一种基于环境与其中存在的各种灾害之间通用关系的协调方法来考虑所有可能性。在本文中,我定义了在通用多风险(GenMR)框架中进行多风险研发和原型设计的概念,即“通用多风险研发和原型设计模板(Generic Multi-Risk R&D and Prototyping Template,GenMR-RPT)”。该数字模板由一个虚拟的自然环境组成,不同的灾害可能在此发生。这些灾害包括地质灾害(地震、滑坡、火山爆发)、水文灾害(河流洪水、风暴潮)、气象灾害(风灾、暴雨)和天文灾害(小行星撞击)。地质灾害和水文灾害都取决于自然环境的特征,这里通过两个因素来定义:地形和土壤。还定义了改变土层的地貌结构。然后,为主要、次要和三级灾害生成危险强度足迹。强调了自然环境对强度足迹和事件级联的作用,例如生成“地震湖”。最后讨论了未来的发展,例如。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/8c3e46258a1e/ijerph-19-16097-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/98e731296aef/ijerph-19-16097-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/5a290505c7db/ijerph-19-16097-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/eccdf853baec/ijerph-19-16097-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/0fb934f90231/ijerph-19-16097-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/9cbf0a73be97/ijerph-19-16097-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/55b9bbdc4f7a/ijerph-19-16097-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/b5e617f5aede/ijerph-19-16097-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/dbd50a343011/ijerph-19-16097-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/8c3e46258a1e/ijerph-19-16097-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/98e731296aef/ijerph-19-16097-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/5a290505c7db/ijerph-19-16097-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/eccdf853baec/ijerph-19-16097-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/0fb934f90231/ijerph-19-16097-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/9cbf0a73be97/ijerph-19-16097-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/55b9bbdc4f7a/ijerph-19-16097-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/b5e617f5aede/ijerph-19-16097-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/dbd50a343011/ijerph-19-16097-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1189/9736322/8c3e46258a1e/ijerph-19-16097-g009.jpg

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