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Decision-Support System for Domestic Water Demand Forecasting and Management

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  • M. Froukh

Abstract

A decision support system (DFMS) for forecastingdomestic water demand including demand management was developedas part of a highly integrated decision-support system forriver-basin management. The system provides water resourcesplanners with the facilities for estimating future water demandfor domestic use for any demand region and time period, havingregard, to the possibility of introducing demand managementmeasures. The system has the capability of predicting domestic-water demand by various methods according to data availability.It can compute conservation effectiveness due to theimplementation of various demand-management measures,forecasting the number of customers for different consumptionunits (person, household and water connection) and facilitatingthe development of demand-scenarios for evaluating variousoptions. In general, the system comprises of the followingcomponents: (1) geographic information system, (2) databasemanagement system, (3) expert system, (4) multi-decisioncomponent, (5) prediction models, (6) user-interface, (7) hypertext files. The system has been tested and demonstratedusing the data of Swindon demand zone of Thames Water Utility. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • M. Froukh, 2001. "Decision-Support System for Domestic Water Demand Forecasting and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 15(6), pages 363-382, December.
    • Handle: RePEc:spr:waterr:v:15:y:2001:i:6:p:363-382
    DOI: 10.1023/A:1015527117823
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    References listed on IDEAS

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    1. J R Davis & I W Grant, 1987. "ADAPT: A Knowledge-Based Decision Support System for Producing Zoning Schemes," Environment and Planning B, , vol. 14(1), pages 53-66, March.
    2. Bonczek, Robert H. & Holsapple, Clyde W. & Whinston, Andrew B., 1981. "Foundations of Decision Support Systems," Elsevier Monographs, Elsevier, edition 1, number 9780121130503 edited by Schmidt, J. William.
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    Cited by:

    1. Dilek Uz & Steven Buck, 2020. "Comparing Water Use Forecasting Model Selection Criteria: The Case of Commercial, Institutional, and Industrial Sector in Southern California," Sustainability, MDPI, vol. 12(10), pages 1-21, May.
    2. Lowry, Gordon & Bianeyin, Felix U. & Shah, Nirav, 2007. "Seasonal autoregressive modelling of water and fuel consumptions in buildings," Applied Energy, Elsevier, vol. 84(5), pages 542-552, May.
    3. Md Haque & Ataur Rahman & Dharma Hagare & Golam Kibria, 2014. "Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1959-1971, May.
    4. Jean-Daniel Rinaudo, 2015. "Long-Term Water Demand Forecasting," Post-Print hal-01183853, HAL.
    5. Abdüsselam Altunkaynak & Mehmet Özger & Mehmet Çakmakci, 2005. "Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(5), pages 641-654, October.
    6. M. Yurdusev & P. O'connell, 2005. "Environmentally-Sensitive Water Resources Planning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(4), pages 375-397, August.
    7. Kostas Bithas & Chrysostomos Stoforos, 2006. "Estimating Urban Residential Water Demand Determinants and Forecasting Water Demand for Athens Metropolitan Area, 2000-2010," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 4(1), pages 47-59.
    8. Mohamed Mohamed & Aysha Al-Mualla, 2010. "Water Demand Forecasting in Umm Al-Quwain (UAE) Using the IWR-MAIN Specify Forecasting Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 4093-4120, November.
    9. Mahmut Firat & Mehmet Yurdusev & Mustafa Turan, 2009. "Evaluation of Artificial Neural Network Techniques for Municipal Water Consumption Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 617-632, March.
    10. Fullerton, Thomas M., Jr. & Ceballos, Alejandro & Walke, Adam G., 2015. "Short-Term Forecasting Analysis for Municipal Water Demand," MPRA Paper 78259, University Library of Munich, Germany, revised 04 Aug 2015.
    11. M. Yurdusev & A. Kumanlıoğlu, 2008. "Survey-Based Estimation of Domestic Water Saving Potential in the Case of Manisa City," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(3), pages 291-305, March.
    12. Aminou Arouna & Stephan Dabbert, 2010. "Determinants of Domestic Water Use by Rural Households Without Access to Private Improved Water Sources in Benin: A Seemingly Unrelated Tobit Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(7), pages 1381-1398, May.
    13. Edy Fradinata & Sakesun Suthummanon & Wannarat Suntiamorntut, 2015. "Forecasting Determinant of Cement Demand in Indonesia with Artificial Neural Network," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 5(7), pages 373-384, July.
    14. Jean-Daniel Rinaudo, 2015. "Long-Term Water Demand Forecasting," Post-Print hal-01290178, HAL.

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