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

Shipeng Chu (), Tuqiao Zhang (), Chengna Xu (), Tingchao Yu () and Yu Shao ()
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Shipeng Chu: Zhejiang University
Tuqiao Zhang: Zhejiang University
Chengna Xu: Zhejiang University
Tingchao Yu: Zhejiang University
Yu Shao: Zhejiang University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 9, No 10, 2863-2878

Abstract: 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.

Keywords: Nodal water demand calibration; Data missing and outlier; Real-time; Water distribution systems (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11269-021-02873-9

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