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abstract

Discovering weekly seasonality for water demand prediction using evolutionary algorithms

Published:15 July 2017Publication History

ABSTRACT

The modern approach to water supply network management and operation is related to the use of modern solutions from both technical and strategic perspectives. Apart from practices promoted by International Water Association (IWA) (active leakage control, pressure management, speed and quality of repair, pipeline and as- sets management), water demand prediction systems are the future. Preparing short-, medium- and long-term water consumption forecasts is the key factor these days. Short-term simulations, mainly those covering the period of 24 or 48 hours, are used to optimise the operation of pumping stations and to resolve current exploitation issues, whereas long-term analyses, covering more than one month or year, are said to support the decision-making process regarding the design and development of water supply networks. Medium-term predictions, covering weeks, are used to create time schedules for the maintenance of water supply networks and develop failure prevention procedures. Due to the lack of assessment teams and proper assessment tools, the majority of water and sewerage companies store registered time series without thorough study of the data. Information regarding the current operating status of the water supply network included in the series is not properly used and irretrievably lost.

References

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  • Published in

    cover image ACM Conferences
    GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2017
    1934 pages
    ISBN:9781450349390
    DOI:10.1145/3067695

    Copyright © 2017 Owner/Author

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 15 July 2017

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