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Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks

Author

Listed:
  • Gustavo Meirelles

    (Campinas State University)

  • Daniel Manzi

    (Campinas State University)

  • Bruno Brentan

    (Campinas State University)

  • Thaisa Goulart

    (Campinas State University)

  • Edevar Luvizotto

Abstract

The success of hydraulic simulation models of water distribution networks is associated with the ability of these models to represent real systems accurately. To achieve this, the calibration phase is essential. Current calibration methods are based on minimizing the error between measured and simulated values of pressure and flow. This minimization is based on a search of parameter values to be calibrated, including pipe roughness, nodal demand, and leakage flow. The resulting hydraulic problem contains several variables. In addition, a limited set of known monitored pressure and flow values creates an indeterminate problem with more variables than equations. Seeking to address the lack of monitored data for the calibration of Water Distribution Networks (WDNs), this paper uses a meta-model based on an Artificial Neural Network (ANN) to estimate pressure on all nodes of a network. The calibration of pipe roughness applies a metaheuristic search method called Particle Swarm Optimization (PSO) to minimize the objective function represented by the difference between simulated and forecasted pressure values. The proposed method is evaluated at steady state and over an extended period for a real District Metering Area (DMA), named Campos do Conde II, and the hypothetical network named C-town, which is used as a benchmark for calibration studies.

Suggested Citation

  • Gustavo Meirelles & Daniel Manzi & Bruno Brentan & Thaisa Goulart & Edevar Luvizotto, 2017. "Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(13), pages 4339-4351, October.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:13:d:10.1007_s11269-017-1750-2
    DOI: 10.1007/s11269-017-1750-2
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    References listed on IDEAS

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    1. Sara Nazif & Mohammad Karamouz & Massoud Tabesh & Ali Moridi, 2010. "Pressure Management Model for Urban 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(3), pages 437-458, February.
    2. S. Liberatore & G. Sechi, 2009. "Location and Calibration of Valves in Water Distribution Networks Using a Scatter-Search Meta-heuristic Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(8), pages 1479-1495, June.
    3. Mehdi Dini & Massoud Tabesh, 2014. "A New Method for Simultaneous Calibration of Demand Pattern and Hazen-Williams Coefficients in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 2021-2034, May.
    4. D. Mora-Melia & P. Iglesias-Rey & F. Martinez-Solano & P. Ballesteros-Pérez, 2015. "Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4817-4831, October.
    5. Ali Haghighi & Helena Ramos, 2012. "Detection of Leakage Freshwater and Friction Factor Calibration in Drinking Networks Using Central Force Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2347-2363, June.
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    Cited by:

    1. Roya Peirovi Minaee & Mehdi Mokhtari & Alireza Moghaddam & Ali Asghar Ebrahimi & Mohsen Askarishahi & Mojtaba Afsharnia, 2019. "Wall Decay Coefficient Estimation in a Real-Life Drinking Water Distribution Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1557-1569, March.
    2. Shipeng Chu & Tuqiao Zhang & Chengna Xu & Tingchao Yu & Yu Shao, 2021. "Dealing with Data Missing and Outlier to Calibrate Nodal Water Demands in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2863-2878, July.
    3. Shipeng Chu & Tuqiao Zhang & Xinhong Zhou & Tingchao Yu & Yu Shao, 2022. "An Efficient Approach for Nodal Water Demand Estimation in Large-scale Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 491-505, January.
    4. Chia-Cheng Shiu & Chih-Chung Chung & Tzuping Chiang, 2024. "Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 323-341, January.

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