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Discovering Reservoir Operating Rules by a Rough Set Approach

Author

Listed:
  • Salvatore Barbagallo
  • Simona Consoli
  • Nello Pappalardo
  • Salvatore Greco
  • Santo Zimbone

Abstract

An integrated Rough Set approach is proposed and implemented to discover the historical operating rules of a Sicilian irrigation purpose reservoir. Operating rules are derived by expressing monthly releases from the reservoir as functions of stored volume, inflow and release during a 35-years period. This is accomplished through the Rough Set approach as implemented in the Rose package and the use of some indices able to recognize and further screen out the effective rules used in water supply reservoir management. This approach represents a new mathematical tool quite different to classical fuzzy rule-based systems in the decision rules induction. Results show that the integrated Rough Set approach allows to individuate with acceptable reliability the real criteria used for the system management. Copyright Springer Science + Business Media, Inc. 2006

Suggested Citation

  • Salvatore Barbagallo & Simona Consoli & Nello Pappalardo & Salvatore Greco & Santo Zimbone, 2006. "Discovering Reservoir Operating Rules by a Rough Set Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(1), pages 19-36, February.
    • Handle: RePEc:spr:waterr:v:20:y:2006:i:1:p:19-36
    DOI: 10.1007/s11269-006-2975-7
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    References listed on IDEAS

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    1. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 2001. "Rough sets theory for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 129(1), pages 1-47, February.
    2. Stam, Antonie & Salewicz, Kazimierz A. & Aronson, Jay E., 1998. "An interactive reservoir management system for Lake Kariba," European Journal of Operational Research, Elsevier, vol. 107(1), pages 119-136, May.
    3. Pereira, Luis Santos & Oweis, Theib & Zairi, Abdelaziz, 2002. "Irrigation management under water scarcity," Agricultural Water Management, Elsevier, vol. 57(3), pages 175-206, December.
    4. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    5. Pawlak, Zdzisaw & Sowinski, Roman, 1994. "Rough set approach to multi-attribute decision analysis," European Journal of Operational Research, Elsevier, vol. 72(3), pages 443-459, February.
    6. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
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    Cited by:

    1. Huaizhi Su & Zhiping Wen & Zhongru Wu, 2011. "Study on an Intelligent Inference Engine in Early-Warning System of Dam Health," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1545-1563, April.
    2. Salvatore Greco & Benedetto Matarazzo & Roman Slowinski & Stelios Zanakis, 2011. "Global investing risk: a case study of knowledge assessment via rough sets," Annals of Operations Research, Springer, vol. 185(1), pages 105-138, May.
    3. Simona Consoli & Benedetto Matarazzo & Nello Pappalardo, 2008. "Operating Rules of an Irrigation Purposes Reservoir Using Multi-Objective Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(5), pages 551-564, May.

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