IDEAS home Printed from https://ideas.repec.org/a/eee/juipol/v90y2024ics0957178724000997.html
   My bibliography  Save this article

Innovative foresight for water utilities asset management using PRISM software

Author

Listed:
  • Nafi, Amir
  • Destandau, François

Abstract

Our analysis aims to better understand water losses by developing a specific methodology for estimating water leak flow rates and durations. Additionally, it aims to improve asset management practices for drinking water utilities by forecasting the costs and benefits of investment and operational decisions on the 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. Applied to four utilities in France, our methodology provides valuable insights for enhancing water loss estimates and finding trade-off asset management policies.

Suggested Citation

  • Nafi, Amir & Destandau, François, 2024. "Innovative foresight for water utilities asset management using PRISM software," Utilities Policy, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:juipol:v:90:y:2024:i:c:s0957178724000997
    DOI: 10.1016/j.jup.2024.101806
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0957178724000997
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jup.2024.101806?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    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. Meireles, Inês & Sousa, Vitor & Matos, José Pedro & Cruz, Carlos Oliveira, 2023. "Determinants of water loss in Portuguese utilities," Utilities Policy, Elsevier, vol. 83(C).
    3. Javier, Prince Joseph Erneszer A. & Liponhay, Marissa P. & Dajac, Carlo Vincienzo G. & Monterola, Christopher P., 2022. "Causal network inference in a dam system and its implications on feature selection for machine learning forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    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. Dongwoo Jang & Hyoseon Park & Gyewoon Choi, 2018. "Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems," Sustainability, MDPI, vol. 10(3), pages 1-13, March.
    6. Kızılöz, Burak & Şişman, Eyüp & Oruç, Halil Nurullah, 2022. "Predicting a water infrastructure leakage index via machine learning," Utilities Policy, Elsevier, vol. 75(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amir Nafi & François Destandau, 2024. "Innovative foresight for water utilities asset management using PRISM software," Post-Print hal-04677293, HAL.
    2. KiJeon Nam & Pouya Ifaei & Sungku Heo & Gahee Rhee & Seungchul Lee & ChangKyoo Yoo, 2019. "An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques," Sustainability, MDPI, vol. 11(10), pages 1-17, May.
    3. Xinxin Liu & Xiaosheng Wang & Haiying Guo & Xiaojie An, 2021. "Benefit Allocation in Shared Water-Saving Management Contract Projects Based on Modified Expected Shapley Value," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 39-62, January.
    4. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    5. Seth Guikema, 2020. "Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1117-1123, June.
    6. Robles-Velasco, Alicia & Cortés, Pablo & Muñuzuri, Jesús & Onieva, Luis, 2020. "Prediction of pipe failures in water supply networks using logistic regression and support vector classification," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    7. Kabir, Golam & Tesfamariam, Solomon & Sadiq, Rehan, 2015. "Predicting water main failures using Bayesian model averaging and survival modelling approach," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 498-514.
    8. Chen, Thomas Ying-Jeh & Guikema, Seth David & Daly, Craig Michael, 2019. "Optimal pipe inspection paths considering inspection tool limitations," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 156-166.
    9. Salehi, Sattar & Robles-Velasco, Alicia & Seyedzadeh, Ali & Ghazali, Aliakbar & Davoudiseresht, Mohsen, 2022. "A hybrid knowledge-based method for pipe renewal planning in Water Distribution Systems with limited data: Application to Iran," Utilities Policy, Elsevier, vol. 78(C).
    10. Tao Peng & Zhiyuan Jin & Lujun Xiao, 2023. "Assessing carrying capacity of regional water resources in karst areas, southwest China: a case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(12), pages 15139-15162, December.
    11. Chen, Thomas Ying-Jeh & Guikema, Seth David, 2020. "Prediction of water main failures with the spatial clustering of breaks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    12. María Molinos-Senante & Alexandros Maziotis, 2019. "Cost Efficiency of English and Welsh Water Companies: a Meta-Stochastic Frontier Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3041-3055, July.
    13. Grzegorz Wrzesiński & Anna Markiewicz, 2022. "Prediction of Permeability Coefficient k in Sandy Soils Using ANN," Sustainability, MDPI, vol. 14(11), pages 1-13, May.
    14. Carlos Oliveira Cruz & Joaquim Miranda Sarmento, 2017. "Reforming traditional PPP models to cope with the challenges of smart cities," Competition and Regulation in Network Industries, , vol. 18(1-2), pages 94-114, March.
    15. Yin Su & Weijun Gao & Dongjie Guan & Tai’an Zuo, 2020. "Achieving Urban Water Security: a Review of Water Management Approach from Technology Perspective," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4163-4179, October.
    16. Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    17. Katarzyna Pietrucha-Urbanik & Janusz R. Rak, 2020. "Consumers’ Perceptions of the Supply of Tap Water in Crisis Situations," Energies, MDPI, vol. 13(14), pages 1-20, July.
    18. Yu, Jin-Zhu & Whitman, Mackenzie & Kermanshah, Amirhassan & Baroud, Hiba, 2021. "A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    19. Zheng Tang & Yijia Li & Xiaofeng Hu & Huanggang Wu, 2019. "Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
    20. Roberto Magini & Manuela Moretti & Maria Antonietta Boniforti & Roberto Guercio, 2023. "A Machine-Learning Approach for Monitoring Water Distribution Networks (WDNs)," Sustainability, MDPI, vol. 15(4), pages 1-17, February.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:juipol:v:90:y:2024:i:c:s0957178724000997. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.sciencedirect.com/journal/utilities-policy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.