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Innovative foresight for water utilities asset management using PRISM software

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  • Amir Nafi

    (ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie - ENGEES - École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg - UNISTRA - Université de Strasbourg - HUS - Les Hôpitaux Universitaires de Strasbourg - INSA Strasbourg - Institut National des Sciences Appliquées - Strasbourg - INSA - Institut National des Sciences Appliquées - CNRS - Centre National de la Recherche Scientifique - MNGE - Matériaux et Nanosciences Grand-Est - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - INSERM - Institut National de la Santé et de la Recherche Médicale - INC-CNRS - Institut de Chimie - CNRS Chimie - CNRS - Centre National de la Recherche Scientifique - Réseau nanophotonique et optique - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - CNRS - Centre National de la Recherche Scientifique)

  • François Destandau

    (SAGE - Sociétés, acteurs, gouvernement en Europe - ENGEES - École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg - UNISTRA - Université de Strasbourg - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

Our article aims at better understanding water losses by developing a specific methodology for estimating the flow rates and durations of water leaks and by improving asset management practices for water utilities by predicting the cost and benefit of investment and operational actions on the drinking water network. Current utility practices are compared to a "do nothing" policy and to the potential effective asset management policies proposed by an artificial neural network (ANN) based software, PRISM. Our methodology has been applied to four utilities in France. The empirical results provide valuable knowledge for improving water loss estimates and the search for trade-off asset management policies.

Suggested Citation

  • Amir Nafi & François Destandau, 2024. "Innovative foresight for water utilities asset management using PRISM software," Post-Print hal-04677293, HAL.
    • Handle: RePEc:hal:journl:hal-04677293
    DOI: 10.1016/j.jup.2024.101806
    Note: View the original document on HAL open archive server: https://hal.science/hal-04677293v1
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    References listed on IDEAS

    as
    1. Michele Mutchek & Eric Williams, 2014. "Moving Towards Sustainable and Resilient Smart Water Grids," Challenges, MDPI, vol. 5(1), pages 1-15, March.
    2. 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.
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