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Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments.

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Title: Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments.
Authors: Nafi, Amir1, Crastes, Eric1, Sadiq, Rehan2, Gilbert, Denis3, Piller, Olivier3
Source: Stochastic Environmental Research & Risk Assessment. Feb2018, Vol. 32 Issue 2, p527-544. 18p.
Subjects: Contamination of drinking water, Backtrack programming, Water distribution, Fuzzy logic, Risk assessment, Charts, diagrams, etc.
Abstract: Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors. [ABSTRACT FROM AUTHOR]
Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments.
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  Data: <searchLink fieldCode="DE" term="%22Contamination+of+drinking+water%22">Contamination of drinking water</searchLink><br /><searchLink fieldCode="DE" term="%22Backtrack+programming%22">Backtrack programming</searchLink><br /><searchLink fieldCode="DE" term="%22Water+distribution%22">Water distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Charts%2C+diagrams%2C+etc%2E%22">Charts, diagrams, etc.</searchLink>
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  Data: Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: Feb2018
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