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Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors

Author

Listed:
  • Caspar V. C. Geelen

    (Wageningen University)

  • Doekle R. Yntema

    (Wetsus, European Centre of Excellence for Sustainable Water Technology)

  • Jaap Molenaar

    (Wageningen University)

  • Karel J. Keesman

    (Wageningen University
    Wetsus, European Centre of Excellence for Sustainable Water Technology)

Abstract

Bursts of drinking water pipes not only cause loss of drinking water, but also damage below and above ground infrastructure. Short-term water demand forecasting is a valuable tool in burst detection, as deviations between the forecast and actual water demand may indicate a new burst. Many of burst detection methods struggle with false positives due to non-seasonal water consumption as a result of e.g. environmental, economic or demographic exogenous influences, such as weather, holidays, festivities or pandemics. Finding a robust alternative that reduces the false positive rate of burst detection and does not rely on data from exogenous processes is essential. We present such a burst detection method, based on Bayesian ridge regression and Random Sample Consensus. Our exogenous nowcasting method relies on signals of all nearby flow and pressure sensors in the distribution net with the aim to reduce the false positive rate. The method requires neither data of exogenous processes, nor extensive historical data, but only requires one week of historical data per flow/pressure sensor. The exogenous nowcasting method is compared with a common water demand forecasting method for burst detection and shows sufficiently higher Nash-Sutcliffe model efficiencies of 82.7% - 90.6% compared to 57.9% - 77.7%, respectively. These efficiency ranges indicate a more accurate water demand prediction, resulting in more precise burst detection.

Suggested Citation

  • Caspar V. C. Geelen & Doekle R. Yntema & Jaap Molenaar & Karel J. Keesman, 2021. "Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1183-1196, March.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:4:d:10.1007_s11269-021-02768-9
    DOI: 10.1007/s11269-021-02768-9
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    References listed on IDEAS

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    1. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-144, April.
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    3. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
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    5. Yuebing Xu & Jing Zhang & Zuqiang Long & Yan Chen, 2018. "A Novel Dual-Scale Deep Belief Network Method for Daily Urban Water Demand Forecasting," Energies, MDPI, vol. 11(5), pages 1-15, April.
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    Cited by:

    1. Wang Pengfei & Jiang Zhiqiang & Duan Jiefeng, 2023. "Burst Analysis of Water Supply Pipe Based on Hydrodynamic Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 2161-2179, March.

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