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Artificial Neural Networks to Forecast Failures in Water Supply Pipes

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  • Alicia Robles-Velasco

    (Departamento Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, 41092 Seville, Spain
    Cátedra del Agua EMASESA-US, 41003 Seville, Spain)

  • Cristóbal Ramos-Salgado

    (Departamento Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, 41092 Seville, Spain)

  • Jesús Muñuzuri

    (Departamento Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, 41092 Seville, Spain)

  • Pablo Cortés

    (Departamento Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, 41092 Seville, Spain)

Abstract

The water supply networks of many countries are experiencing a drastic increase in the number of pipe failures. To reverse this tendency, it is essential to optimise the replacement plans of pipes. For this reason, companies demand pioneering techniques to predict which pipes are more prone to fail. In this study, an Artificial Neural Network (ANN) is designed to classify pipes according to their predisposition to fail based on physical and operational input variables. In addition, the usefulness and effectiveness of two sampling methods, under-sampling and over-sampling, are analysed. The implementation of the model is done using the open-source software Weka, which is specialised in machine-learning algorithms. The system is tested with a database from a real water network in Spain, obtaining high-accurate results. It is verified that the balance of the training set is imperative to increase the predictions’ accurateness. Furthermore, under-sampling prioritises true positive rates, whereas over-sampling makes the system learn to predict failures and non-failures with the same precision.

Suggested Citation

  • Alicia Robles-Velasco & Cristóbal Ramos-Salgado & Jesús Muñuzuri & Pablo Cortés, 2021. "Artificial Neural Networks to Forecast Failures in Water Supply Pipes," Sustainability, MDPI, vol. 13(15), pages 1-10, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8226-:d:599864
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    References listed on IDEAS

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    1. 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).
    2. Symeon Christodoulou & Alexandra Deligianni, 2010. "A Neurofuzzy Decision Framework for the Management of Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 139-156, January.
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    Cited by:

    1. Jose-Luis Molina & Carmen Patino-Alonso & Xi Wan & Raziyeh Farmani, 2025. "StocHastIc Early Leakage Detection System (SHIELDS) 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. 39(8), pages 4189-4204, June.

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