| Autor: |
Michael Beer, Edoardo Patelli, Silvia Tolo |
| Rok vydání: |
2014 |
| Předmět: |
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| Zdroj: |
Vulnerability, Uncertainty, and Risk. |
| DOI: |
10.1061/9780784413609.061 |
| Popis: |
The potentiality of natural hazards to trigger simultaneous failures, leading to technological disasters (i.e. also referred as Natech events), requires tools and methods able to realistically simulate events and interactions between natural phenomena and simultaneous technological failures. The task of the present paper is to highlight the advantages and potentialities of Bayesian Networks (BNs) as a risk assessment tool for simultaneous failures triggered by extreme natural events. For this purpose, a simplified BN model for the risk assessment of a spent nuclear fuel pond of facilities subject to extreme weather conditions is provided. The network is then applied to a real-case study integrating different data-set and models available in literature to show the extreme flexibility of this approach and its ability to integrate climate change projections to evaluate future risks. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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