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Dealing with Data Missing and Outlier to Calibrate Nodal Water Demands in Water Distribution Systems

Author

Listed:
  • Shipeng Chu

    (Zhejiang University)

  • Tuqiao Zhang

    (Zhejiang University)

  • Chengna Xu

    (Zhejiang University)

  • Tingchao Yu

    (Zhejiang University)

  • Yu Shao

    (Zhejiang University)

Abstract

Model parameters of the water distribution system (WDS) such as nodal water demands, should be carefully calibrated by measurements. However, the inconvenience of data missing and outliers is a common feature of real-time measurements, and significantly reduces the robustness and usability of WDS models. Hence, dealing with these uncertainties in WDSs is still a challenge that needs to be tackled. This paper developed an approach to detect and compensate for the missing data and outliers in real-time. Then the compensated data are fused by a Bayesian method to calibrate the nodal water demand. The developed approach is validated by using a simple network and a realistic network. The results demonstrate that the developed approach can effectively improve the robustness of the calibration algorithm in the presence of data missing and outliers.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02873-9
    DOI: 10.1007/s11269-021-02873-9
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Li, Qian & Liu, Xinzhi & Zhu, Qingxin & Zhong, Shouming & Zhang, Dian, 2019. "Distributed state estimation for stochastic discrete-time sensor networks with redundant channels," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 230-246.
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