The Management of Na-Tech Risk Using Bayesian Network
In the last decades, the frequency and severity of Natural-Technological events (i.e., industrial accidents triggered by natural phenomena or Na-Techs) increased. These could be more severe than simple technological accidents because the natural phenomenon could cause the prevention/mitigation/emerg...
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doaj-5c5c92d75a514f29b50f9b0a25c679102021-07-23T14:12:14ZengMDPI AGWater2073-44412021-07-01131966196610.3390/w13141966The Management of Na-Tech Risk Using Bayesian NetworkGiuseppa Ancione0Maria Francesca Milazzo1Department of Engineering, University of Messina, Contrada di Dio, 98166 Messina, ItalyDepartment of Engineering, University of Messina, Contrada di Dio, 98166 Messina, ItalyIn the last decades, the frequency and severity of Natural-Technological events (i.e., industrial accidents triggered by natural phenomena or Na-Techs) increased. These could be more severe than simple technological accidents because the natural phenomenon could cause the prevention/mitigation/emergency systems fail. The dynamic assessment of the risk associated with these events is essential for a more effective prevention and mitigation of the consequences and emergency preparation. The main goal of this study is the development of a fast and dynamic tool for the risk manager. An approach supporting the management of the consequence is presented. It is based on the definition of a risk-related index, presented in the form of a discrete variable that combines frequency and magnitude of the events and other factors contributing to the worsening of Na-Tech. A properly designed Geographical Information System (GIS) allows the collection and processing of territorial information with the aim to create new data contributing to the quantification of the Na-Tech risk index. A Bayesian network has been built which efficiently lends in including within the model multiple elements with a direct or indirect impact on the distribution of risk levels. By means of this approach, a dynamic updating of the risk index is made. The proposed approach has been applied to an Italian case-study.https://www.mdpi.com/2073-4441/13/14/1966natural-technological eventshazardous materialchemical industrydynamic risk assessmentrisk managementflood |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Giuseppa Ancione Maria Francesca Milazzo |
spellingShingle |
Giuseppa Ancione Maria Francesca Milazzo The Management of Na-Tech Risk Using Bayesian Network Water natural-technological events hazardous material chemical industry dynamic risk assessment risk management flood |
author_facet |
Giuseppa Ancione Maria Francesca Milazzo |
author_sort |
Giuseppa Ancione |
title |
The Management of Na-Tech Risk Using Bayesian Network |
title_short |
The Management of Na-Tech Risk Using Bayesian Network |
title_full |
The Management of Na-Tech Risk Using Bayesian Network |
title_fullStr |
The Management of Na-Tech Risk Using Bayesian Network |
title_full_unstemmed |
The Management of Na-Tech Risk Using Bayesian Network |
title_sort |
management of na-tech risk using bayesian network |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2021-07-01 |
description |
In the last decades, the frequency and severity of Natural-Technological events (i.e., industrial accidents triggered by natural phenomena or Na-Techs) increased. These could be more severe than simple technological accidents because the natural phenomenon could cause the prevention/mitigation/emergency systems fail. The dynamic assessment of the risk associated with these events is essential for a more effective prevention and mitigation of the consequences and emergency preparation. The main goal of this study is the development of a fast and dynamic tool for the risk manager. An approach supporting the management of the consequence is presented. It is based on the definition of a risk-related index, presented in the form of a discrete variable that combines frequency and magnitude of the events and other factors contributing to the worsening of Na-Tech. A properly designed Geographical Information System (GIS) allows the collection and processing of territorial information with the aim to create new data contributing to the quantification of the Na-Tech risk index. A Bayesian network has been built which efficiently lends in including within the model multiple elements with a direct or indirect impact on the distribution of risk levels. By means of this approach, a dynamic updating of the risk index is made. The proposed approach has been applied to an Italian case-study. |
topic |
natural-technological events hazardous material chemical industry dynamic risk assessment risk management flood |
url |
https://www.mdpi.com/2073-4441/13/14/1966 |
work_keys_str_mv |
AT giuseppaancione themanagementofnatechriskusingbayesiannetwork AT mariafrancescamilazzo themanagementofnatechriskusingbayesiannetwork AT giuseppaancione managementofnatechriskusingbayesiannetwork AT mariafrancescamilazzo managementofnatechriskusingbayesiannetwork |
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