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

Alessandro Pagano (), Irene Pluchinotta (), Raffaele Giordano (), Anna Bruna Petrangeli (), Umberto Fratino () and Michele Vurro ()
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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)

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 6, No 11, 2145 pages

Abstract: 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.

Keywords: Emergency management; Drinking water supply systems; Bayesian belief networks; Uncertainty analysis; Decision support system (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11269-018-1922-8

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