Pressure Sampling Design for Estimating Nodal Water Demand in Water Distribution Systems

Yu Shao (), Kun Li (), Tuqiao Zhang (), Weilin Ao () and Shipeng Chu ()
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Yu Shao: Zhejiang University, Key Laboratory of Drinking Water Safety and Distribution Technology of Zhejiang Province
Kun Li: Zhejiang University, Key Laboratory of Drinking Water Safety and Distribution Technology of Zhejiang Province
Tuqiao Zhang: Zhejiang University, Key Laboratory of Drinking Water Safety and Distribution Technology of Zhejiang Province
Weilin Ao: SiChuan Communication Surveying & Design Institute Co., LTD
Shipeng Chu: Zhejiang University, Key Laboratory of Drinking Water Safety and Distribution Technology of Zhejiang Province

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 4, No 17, 1527 pages

Abstract: Abstract The water distribution system (WDS) hydraulic model is extensively used for design and management of WDS. The nodal water demand is the crucial parameter of the model that requires accurate estimating by the pressure measurements. Proper pressure sampling design is essential for estimating nodal water demand and improving model accuracy. Existing research has emphasized the need to enhance the observability of monitoring systems and mitigate the adverse effects of monitoring noise. However, methods that simultaneously consider both of these factors in sampling design have not been adequately studied. In this study, a novel two-objective sampling design method is developed to improve the system observability and mitigate the adverse effects of monitoring noise. The approach is applied to a realistic network and results demonstrate that the developed approach can effectively improve the observability and robustness of the system especially when considerable measurement noise is considered.

Keywords: Sampling design; Condition number; Hessian matrix; Measurement data noise; Demand estimation; Water distribution system (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03736-9

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