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Predictive Temporal Data-Mining Approach for Evolving Knowledge Based Reservoir Operation Rules

Author

Listed:
  • S. Mohan

    (Environmental and Water Resources Engineering, Department of Civil Engineering, Indian Institute of Technology Madras)

  • N. Ramsundram

    (Environmental and Water Resources Engineering, Department of Civil Engineering, Indian Institute of Technology Madras)

Abstract

The persistent problem in reservoir operation is that the derived optimal releases fail to incorporate the decision maker or reservoir operators’ knowledge into reservoir operation models. The reservoir operators’ knowledge is specific to that particular reservoir and incorporating such an experienced knowledge will help to derive field reality based operation rules. The available historical reservoir operation databases are the representative samples of reservoir operators’ knowledge or experience. Thus, an attempt has been made that deals with the development of a methodological framework to recover or explore the historical reservoir operation database to derive the reservoir operators’ knowledge as operational rules. The developed methodological framework utilizes the strength and capability of recently developed predictive datamining algorithms to recover the knowledge from large historical database. Predictive data-mining algorithms such as a) classifier: Artificial Neural Network (ANN), and b) regression: Support Vector Regression (SVR) have been used for single reservoir operation data-mining (SROD) modelling framework to explore the temporal dependence between different variables of reservoir operation. The rules of operation or knowledge learned from the training database have been used as guiding rules for predicting the future reservoir operators’ decision on operating the reservoir for the given condition on the inflow, initial storage, and demand requirements. The developed SROD model was found to be efficient in exploring the hidden relationships that exist in a single reservoir system.

Suggested Citation

  • S. Mohan & N. Ramsundram, 2016. "Predictive Temporal Data-Mining Approach for Evolving Knowledge Based Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3315-3330, August.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:10:d:10.1007_s11269-016-1351-5
    DOI: 10.1007/s11269-016-1351-5
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    References listed on IDEAS

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    1. V. Chandramouli & Paresh Deka, 2005. "Neural Network Based Decision Support Model for Optimal Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(4), pages 447-464, August.
    2. Paresh Shirsath & Anil Singh, 2010. "A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(8), pages 1571-1581, June.
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