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The Analysis of Water Supply Operating Conditions Systems by Means of Empirical Exponents

Institute of Environmental Engineering, Wroclaw University of Environmental and Life Sciences, Grunwaldzki 24, Wrocław 50-363, Poland
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Water 2019, 11(12), 2452; https://doi.org/10.3390/w11122452
Received: 4 October 2019 / Revised: 10 November 2019 / Accepted: 20 November 2019 / Published: 22 November 2019
(This article belongs to the Section Urban Water Management)
The stochastic character of water consumption by consumers and the technical condition of water supply systems are the main deterministic random factors influencing the observed changes in flow rate and pressure. The implementation of Supervisory Control and Data Acquisition (SCADA) systems resulted in the creation of dispersed data sets coming from the devices controlling the operation of the water supply system. Thanks to the use of metadata and advanced computer systems of analysis, data from various sources can be analyzed to detect the operating conditions of the water supply system. The aim of the research was to analyze an empirical exponent, determined on the basis of flow rate and pressure measurements for one of the District Metered Areas (DMAs). Modern supervised and unsupervised machine learning systems were implemented to classify the obtained results. The results of the research showed that on the basis of the established empirical exponent in the systems in which the pressure is reduced at night, it is possible to qualify the operating conditions of the water supply system in the DMA with accuracy of up to 90%. The conducted tests may be implemented as a component of expert diagnostic systems in water companies. View Full-Text
Keywords: district metered area (DMA); empirical exponent; machine learning; monitoring; water supply systems district metered area (DMA); empirical exponent; machine learning; monitoring; water supply systems
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Stańczyk, J.; Burszta-Adamiak, E. The Analysis of Water Supply Operating Conditions Systems by Means of Empirical Exponents. Water 2019, 11, 2452.

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