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

  • Amir Nafi
  • Eric Crastes
  • Rehan Sadiq
  • Denis Gilbert
  • Olivier Piller
Original Paper

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.

Keywords

Risk Vulnerability Sensitivity Backtracking Intentional contamination Fuzzy logic Aggregation Water distribution network Security 

Notes

Acknowledgements

The work presented in the paper is part of the French-German collaborative research project SMart-OnlineWDN that is funded by the French National Research Agency (ANR Project: ANR-11-SECU-006) and the German Federal Ministry of Education and Research (BMBF; Project: 13N12180).

References

  1. Ailamaki A, Faloutos C, Fischbeck PS, Small MJ, VanBriesen J (2003) An environmental sensor network to determine drinking water quality and security. ACM SIGMOD Rec 32(4):47–52CrossRefGoogle Scholar
  2. American Society for Mechanical Engineering (ASME) (2006) RAMCAP: the framework (version 2.0). ASME Innovative Technologies Institute, LLCGoogle Scholar
  3. Augeraud P, Touaty M (2002) Consommation d’eau par les secteurs industriels. Planistat France. Rapport finalGoogle Scholar
  4. Bernard R, Bouyssou D (1993) Aide multicritère à la décision: Méthodes et cas, Paris, Economica, ISBN 2-7178-2473-1Google Scholar
  5. Cingolani P, Alcala-Fdez J (2012) jFuzzyLogic: a robust and flexible fuzzy-logic inference system language implementation. In: FUZZ-IEEE, pp 1–8. Citeseer. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.415.3325&rep=rep1&type=pdf. Accessed 31 Aug 2015
  6. Clark R, Chandrasekaran L, Buchberger S (2008) Modeling the propagation of waterborne disease in water distribution systems: results from a case study. 8th water distribution systems analysis symposium 2006, Cincinnati, USA, pp 1–20. doi: 10.1061/40941(247)71
  7. Copeland C (2010) Terrorism and security issues facing the water infrastructure sector. In: Report for congress, congressional research service, order code RS21026, Washington, DC, USA. https://www.fas.org/sgp/crs/terror/RL32189.pdf. Accessed May 10 2016
  8. Di Nardo A, Di Natale M, Guida M, Musmarra D (2013) Water network protection from intentional contamination by sectorization. Water Resour Manag 27(6):1837–1850CrossRefGoogle Scholar
  9. Di Nardo A, Di Natale M, Musmarra D, Santonastaso GF, Tzatchkov V, Alcocer-Yamanaka V-H (2014) A district sectorization for water network protection from intentional contamination. 12th international conference on computing and control for the water industry, CCWI2013. Procedia Engineering, vol 70, pp 515–524. doi: 10.1016/j.proeng.2014.02.057
  10. Ezell BC, Farr J, Wiese I (2000) Infrastructure risk analysis of municipal water distribution system. J Infrastruct Syst 6(3):118–122CrossRefGoogle Scholar
  11. Figueira J, Roy B (2002) Determining the weights of criteria in the ELECTRE type methods with a revised Simos procedure. Eur J Oper Res 139(2):317–326CrossRefGoogle Scholar
  12. Francisque A, Rodriguez MJ, Sadiq R, Miranda LF, Proulx F (2009) Prioritizing monitoring locations in a water distribution network: a fuzzy risk approach. J Water Suppl Res Technol AQUA 58(7):488–509CrossRefGoogle Scholar
  13. Hall J, Zaffiro AD, Marx RB, Kefauver PC, Krishnan ER, Herrmann JG (2007) Online water quality parameters as indicators of distribution system contamination. J Am Water Works Assoc 99(1):66–77Google Scholar
  14. Hart WE, Murray R (2010) Review of sensor placement strategies for contamination warning systems in drinking water distribution systems. J Water Resour Plan Manag 136(6):611–619CrossRefGoogle Scholar
  15. Islam N, Farahat A, Al-Zahrani MAM, Rodriguez MJ, Sadiq R (2015) Contaminant intrusion in water distribution networks: review and proposal of an integrated model for decision making. Environ Rev 23(3):337–352CrossRefGoogle Scholar
  16. Murray RE, Grayman WM, Savic DA, Farmani R (2010) Effects of DMA redesign on water distribution system performance. Integrating water systems—Boxall & Maksimovíc (eds)© 2010Taylor & Francis Group, London, ISBN 978-0-415-54851-9Google Scholar
  17. Nilsson KA, Buchberger SG, Clark RM (2005) Simulating exposures to deliberate intrusions into water distribution systems. J Water Resour Plan Manag 131(3):228–236CrossRefGoogle Scholar
  18. Panigrahi DP, Mujumdar PP (2000) Reservoir operation modeling with fuzzy logic. Water Res Manag 14:89–109CrossRefGoogle Scholar
  19. Porteau (2016) Porteau 4.0, Logiciel de modélisation hydraulique. http://porteau.irstea.fr/. Accessed May 10, 2016
  20. Rasekh A, Brumbelow K (2013) Probabilistic analysis and optimization to characterize critical water distribution system contamination scenarios. J Water Res Plan Manag 139(2):191–199CrossRefGoogle Scholar
  21. Sadiq R, Kleiner Y, Rajani B (2007) Water quality failures in distribution networks—risk analysis using fuzzy logic and evidential reasoning. Risk Anal 27(5):1381–1394CrossRefGoogle Scholar
  22. Simos J (1990) L’évaluation Environnementale: Un Processus Cognitif Négocié. Thèse de doctorat. DGF-Lausanne, SuisseGoogle Scholar
  23. SMaRT-onlineWDN (2015) http://www.smart-onlinewdn.eu/. Last visit on 5 Oct 2015
  24. Tchórzewska-Cieślak B (2011) Fuzzy failure risk analysis in drinking water technical system. Reliab Theory Appl 1(20):138–148Google Scholar
  25. Tesfamariam S, Sadiq R (2006) Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP). Stoch Environ Res Risk Assess 21(1):35–50CrossRefGoogle Scholar
  26. Torres JM, Brumbelow K, Guikema SD (2009) Risk classification and uncertainty propagation for virtual water distribution systems. Reliab Eng Syst Saf 94(8):1259–1273CrossRefGoogle Scholar
  27. Ung H (2016) Quasi real-time model for security of water distribution network. Modeling and simulation. Université de Bordeaux, PhD Thesis. https://tel.archives-ouvertes.fr/tel-01310849/. Accessed Mar 2017
  28. Ung H, Piller O, Gilbert D, Mortazavi I (2013) Inverse transport method for determination of potential contamination sources with a stochastic framework. In: World environmental and water resources congress, ASCE, pp 798–812. http://ascelibrary.org/doi/abs/10.1061/9780784412947.077. Accessed 28 Aug 2015
  29. US EPA (2003) Response protocol toolbox: planning for and responding to drinking water contamination threats and incidents. http://www.epa.gov/safewater/watersecurity/pubs/guide_response_overview.pdf. Accessed 28 Aug 2015
  30. Xu Z (2005) An overview of methods for determining OWA weights. Int J Intell Syst 20(8):843–865CrossRefGoogle Scholar
  31. Yager RR (1998) New modes of OWA information fusion. Int J Intell Syst 13(7):661–681CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Amir Nafi
    • 1
  • Eric Crastes
    • 1
  • Rehan Sadiq
    • 2
  • Denis Gilbert
    • 3
  • Olivier Piller
    • 3
  1. 1.Joint research unit Territorial Management of Water and the Environment (GESTE) Irstea-EngeesStrasbourg CedexFrance
  2. 2.School of Engineering University of British Columbia, Okanagan CampusKelownaCanada
  3. 3.Water Infrastructure Asset Management Team, Water Department, ETBX Research UnitIrsteaCestasFrance

Personalised recommendations