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Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems

Author

Listed:
  • Dongwoo Jang

    (Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea)

  • Hyoseon Park

    (Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea)

  • Gyewoon Choi

    (Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea)

Abstract

Leaks in a water distribution network (WDS) constitute losses of water supply caused by pipeline failure, operational loss, and physical factors. This has raised the need for studies on the factors affecting the leakage ratio and estimation of leakage volume in a water supply system. In this study, principal component analysis (PCA) and artificial neural network (ANN) were used to estimate the volume of water leakage in a WDS. For the study, six main effective parameters were selected and standardized data obtained through the Z-score method. The PCA-ANN model was devised and the leakage ratio was estimated. An accuracy assessment was performed to compare the measured leakage ratio to that of the simulated model. The results showed that the PCA-ANN method was more accurate for estimating the leakage ratio than a single ANN simulation. In addition, the estimation results differed according to the number of neurons in the ANN model’s hidden layers. In this study, an ANN with multiple hidden layers was found to be the best method for estimating the leakage ratio with 12–12 neurons. This suggested approaches to improve the accuracy of leakage ratio estimation, as well as a scientific approach toward the sustainable management of water distribution systems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:750-:d:135456
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Kızılöz, Burak & Şişman, Eyüp & Oruç, Halil Nurullah, 2022. "Predicting a water infrastructure leakage index via machine learning," Utilities Policy, Elsevier, vol. 75(C).
    6. Grzegorz Wrzesiński & Anna Markiewicz, 2022. "Prediction of Permeability Coefficient k in Sandy Soils Using ANN," Sustainability, MDPI, vol. 14(11), pages 1-13, May.
    7. 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.

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