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Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events

Author

Listed:
  • Alessandro Pagano

    (Water Research Institute – National Research Council (IRSA-CNR))

  • Irene Pluchinotta

    (LAMSADE – CNRS, Univ. Paris-Dauphine, PSL Research Univ)

  • Raffaele Giordano

    (Water Research Institute – National Research Council (IRSA-CNR))

  • Anna Bruna Petrangeli

    (Water Research Institute – National Research Council (IRSA-CNR))

  • Umberto Fratino

    (Politecnico di Bari)

  • Michele Vurro

    (Water Research Institute – National Research Council (IRSA-CNR))

Abstract

The availability and the quality of drinking water are key requirements for the well-being and the safety of a community, both in ordinary conditions and in case of disasters. Providing safe drinking water in emergency contributes to limit the intensity and the duration of crises, and is thus one of the main concerns for decision-makers, who operate under significant uncertainty. The present work proposes a Decision Support System for the emergency management of drinking water supply systems, integrating: i) a vulnerability assessment model based on Bayesian Belief Networks with the related uncertainty assessment model; ii) a model for impact, and related uncertainty assessment, based on Bayesian Belief Networks. The results of these models are jointly analyzed, providing decision-makers with a ranking of the priority of intervention. A GIS interface (G-Net) is developed to manage both input spatial information and results. The methodology is implemented in L’Aquila case study, discussing the potentialities associated to the use of the tool dealing with information and data uncertainty.

Suggested Citation

  • Alessandro Pagano & Irene Pluchinotta & Raffaele Giordano & Anna Bruna Petrangeli & Umberto Fratino & Michele Vurro, 2018. "Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(6), pages 2131-2145, April.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:6:d:10.1007_s11269-018-1922-8
    DOI: 10.1007/s11269-018-1922-8
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    References listed on IDEAS

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    1. Rosa Sobradelo & Joan Martí & Christopher Kilburn & Carmen López, 2015. "Probabilistic approach to decision-making under uncertainty during volcanic crises: retrospective application to the El Hierro (Spain) 2011 volcanic crisis," 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. 76(2), pages 979-998, March.
    2. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
    3. Ludovic Gaudard & Franco Romerio, 2015. "Natural hazard risk in the case of an emergency: the real options’ approach," 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. 75(1), pages 473-488, January.
    4. Francis, Royce A. & Guikema, Seth D. & Henneman, Lucas, 2014. "Bayesian Belief Networks for predicting drinking water distribution system pipe breaks," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 1-11.
    5. Tiku T. Tanyimboh, 2017. "Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3189-3204, August.
    6. 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.
    7. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    8. 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.
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    Cited by:

    1. Vanessa Assumma & Marta Bottero & Giulia Datola & Elena De Angelis & Roberto Monaco, 2019. "Dynamic Models for Exploring the Resilience in Territorial Scenarios," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    2. Khalil Ardeshirtanha & Ahmad Sharafati, 2020. "Assessment of Water Supply Dam Failure Risk: Development of New Stochastic Failure Modes and Effects Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1827-1841, March.
    3. Andrea Di Ronco & Francesca Giacobbo & Antonio Cammi, 2020. "A Kalman Filter-Based Approach for Online Source-Term Estimation in Accidental Radioactive Dispersion Events," Sustainability, MDPI, vol. 12(23), pages 1-19, November.
    4. Mahsa Ghandi & Abbas Roozbahani, 2020. "Risk Management of Drinking Water Supply in Critical Conditions Using Fuzzy PROMETHEE V Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 595-615, January.
    5. Alessandro Pagano & Raffaele Giordano & Michele Vurro, 2021. "A Decision Support System Based on AHP for Ranking Strategies to Manage Emergencies on Drinking Water Supply Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 613-628, January.

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