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A Bayesian vulnerability assessment tool for drinking water mains under extreme events

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Listed:
  • Alessandro Pagano
  • Raffaele Giordano
  • Ivan Portoghese
  • Umberto Fratino
  • Michele Vurro

Abstract

Drinking water security is a life safety issue as an adequate supply of safe water is essential for economic, social and sanitary reasons. Damage to any element of a water system, as well as corruption of resource quality, may have significant effects on the population it serves and on all other dependent resources and activities. As well as an analysis of the reliability of water distribution systems in ordinary conditions, it is also crucial to assess system vulnerability in the event of natural disasters and of malicious or accidental anthropogenic acts. The present work summarizes the initial results of research activities that are underway with the intention of developing a vulnerability assessment methodology for drinking water infrastructures subject to hazardous events. The main aim of the work was therefore to provide decision makers with an effective operational tool which could support them mainly to increase risk awareness and preparedness and, possibly, to ease emergency management. The proposed tool is based on Bayesian Belief Networks (BBN), a probabilistic methodology which has demonstrated outstanding potential to integrate a range of sources of knowledge, a great flexibility and the ability to handle in a mathematically sound way uncertainty due to data scarcity and/or limited knowledge of the system to be managed. The tool was implemented to analyze the vulnerability of two of the most important water supply systems in the Apulia region (southern Italy) which have been damaged in the past by natural hazards. As well as being useful for testing and improving the predictive capabilities of the methodology and for possibly modifying its structure and features, the case studies have also helped to underline its strengths and weaknesses. Particularly, the experiences carried out demonstrated how the use of BBN was consistent with the lack of data reliability, quality and accessibility which are typical of complex infrastructures, such as the water distribution networks. The potential applications and future developments of the proposed tool have been also discussed accordingly. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Alessandro Pagano & Raffaele Giordano & Ivan Portoghese & Umberto Fratino & Michele Vurro, 2014. "A Bayesian vulnerability assessment tool for drinking water mains under extreme events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 2193-2227, December.
    • Handle: RePEc:spr:nathaz:v:74:y:2014:i:3:p:2193-2227
    DOI: 10.1007/s11069-014-1302-5
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    1. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
    2. Alik Ismail-Zadeh & Kuniyoshi Takeuchi, 2007. "Preventive disaster management of extreme natural events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 42(3), pages 459-467, September.
    3. Batchelor, Charles & Cain, Jeremy, 1999. "Application of belief networks to water management studies," Agricultural Water Management, Elsevier, vol. 40(1), pages 51-57, March.
    4. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    5. Symeon Christodoulou, 2011. "Water Network Assessment and Reliability Analysis by Use of Survival Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(4), pages 1229-1238, March.
    6. Jose-Luis Molina & Raziyeh Farmani & John Bromley, 2011. "Aquifers Management through Evolutionary Bayesian Networks: The Altiplano Case Study (SE Spain)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3883-3909, November.
    7. Ban-Jwu Shih & Che-Hao Chang, 2006. "Damage Survey of Water Supply Systems and Fragility Curve of PVC Water Pipelines in the Chi–Chi Taiwan Earthquake," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 37(1), pages 71-85, February.
    8. Budescu, David V. & Wallsten, Thomas S., 1985. "Consistency in interpretation of probabilistic phrases," Organizational Behavior and Human Decision Processes, Elsevier, vol. 36(3), pages 391-405, December.
    9. Warner Marzocchi & Alexander Garcia-Aristizabal & Paolo Gasparini & Maria Mastellone & Angela Di Ruocco, 2012. "Basic principles of multi-risk assessment: a case study in Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(2), pages 551-573, June.
    10. M. Geertsema & J. Schwab & A. Blais-Stevens & M. Sakals, 2009. "Landslides impacting linear infrastructure in west central British Columbia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 48(1), pages 59-72, January.
    11. E. Binaghi & M. Boschetti & P.A. Brivio & I. Gallo & F. Pergalani & A. Rampini, 2004. "Prediction of Displacements in Unstable Areas Using a Neural Model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 32(1), pages 135-154, May.
    12. Jianxiu Wang & Xueying Gu & Tianrong Huang, 2013. "Using Bayesian networks in analyzing powerful earthquake disaster chains," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 509-527, September.
    13. M. Peng & L. Zhang, 2012. "Analysis of human risks due to dam break floods—part 2: application to Tangjiashan landslide dam failure," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(2), pages 1899-1923, November.
    14. M. Peng & L. Zhang, 2012. "Analysis of human risks due to dam-break floods—part 1: a new model based on Bayesian networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(1), pages 903-933, October.
    15. Michalis Fragiadakis & Symeon Christodoulou & Dimitrios Vamvatsikos, 2013. "Reliability Assessment of Urban Water Distribution Networks Under Seismic Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3739-3764, August.
    16. Gema Carmona & Consuelo Varela-Ortega & John Bromley, 2011. "The Use of Participatory Object-Oriented Bayesian Networks and Agro-Economic Models for Groundwater Management in Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1509-1524, March.
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    3. Apurva Pamidimukkala & Sharareh Kermanshachi & Nikhitha Adepu & Elnaz Safapour, 2021. "Resilience in Water Infrastructures: A Review of Challenges and Adoption Strategies," Sustainability, MDPI, vol. 13(23), pages 1-15, November.
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    5. Dawid Szpak, 2020. "Method for Determining the Probability of a Lack of Water Supply to Consumers," Energies, MDPI, vol. 13(20), pages 1-16, October.
    6. Kamrani, Kazem & Roozbahani, Abbas & Hashemy Shahdany, Seied Mehdy, 2020. "Using Bayesian networks to evaluate how agricultural water distribution systems handle the water-food-energy nexus," Agricultural Water Management, Elsevier, vol. 239(C).
    7. Massoud Tabesh & Abbas Roozbahani & Bardia Roghani & Niousha Rasi Faghihi & Reza Heydarzadeh, 2018. "Risk Assessment of Factors Influencing Non-Revenue Water Using Bayesian Networks and Fuzzy Logic," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3647-3670, September.
    8. Krzysztof Boryczko & Janusz Rak, 2020. "Method for Assessment of Water Supply Diversification," Resources, MDPI, vol. 9(7), pages 1-15, July.

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