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A Comprehensive Approach for Spatial and Temporal Water Demand Profiling to Improve Management in Network Areas

Author

Listed:
  • Dália Loureiro

    (Urban Water Unit, National Civil Engineering Laboratory)

  • Aisha Mamade

    (Universidade de Lisboa)

  • Marta Cabral

    (Urban Water Unit, National Civil Engineering Laboratory)

  • Conceição Amado

    (Universidade de Lisboa)

  • Dídia Covas

    (Universidade de Lisboa)

Abstract

The aim of this paper is to present a comprehensive approach for spatial and temporal demand profiling in water distribution systems. Multiple linear regression models for estimating network design parameters and decision trees for predicting daily demand patterns are presented. Proposed approach is a four-step procedure: data collection, data processing, data characterization, and spatial and temporal demand profiling. Continuous flow measurements and infrastructure and billing data were collected from a large set of water network areas and combined with census data. Main results indicate that family structures (i.e., families with elderly or adolescents), individuals’ mobility (i.e., people employed in the tertiary sector and university graduates) and public consumption (i.e., public spaces’ irrigation) are key-variables to profile water demand. Profiling models are of the utmost importance to describe water demand in areas with no monitoring but with similar socio-demographic characteristics to the ones analyzed, to improve network operation and to support network planning and design in new areas. Obtained models have been tested for new areas, showing good prediction performances.

Suggested Citation

  • Dália Loureiro & Aisha Mamade & Marta Cabral & Conceição Amado & Dídia Covas, 2016. "A Comprehensive Approach for Spatial and Temporal Water Demand Profiling to Improve Management in Network Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3443-3457, August.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:10:d:10.1007_s11269-016-1361-3
    DOI: 10.1007/s11269-016-1361-3
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    2. Roberta Padulano & Giuseppe Giudice, 2018. "A Mixed Strategy Based on Self-Organizing Map for Water Demand Pattern Profiling of Large-Size Smart Water Grid Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3671-3685, September.
    3. Roberta Padulano & Giuseppe Giudice, 2019. "Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 739-755, January.

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