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Fuzzy Conceptual Hydrological Model for Water Flow Prediction

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  • Mustafa Turan
  • Mehmet Yurdusev

Abstract

Reliability in flow prediction is key to designing water resources projects. Over prediction may result in overdesign whereas under prediction brings about insufficient capacity solutions. While the former means insufficient use of financial resources, the latter may result in some water demand unmet. Therefore, so many techniques have been developed and used to make better flow prediction. In this study, this traditional problem is revisited in an attempt to improve the modeling performance of long used conceptual hydrological models. This is attained by incorporating fuzzy systems into a presently used conceptual model. The fuzzy integration process is carried out through the replacement of the storage elements of conceptual model by fuzzy systems. The case study undertaken has proved that the fuzzy conceptual model developed is quite competitive with ordinary conceptual model and promises improved predictions. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Mustafa Turan & Mehmet Yurdusev, 2016. "Fuzzy Conceptual Hydrological Model for Water Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 653-667, January.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:2:p:653-667
    DOI: 10.1007/s11269-015-1183-8
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    References listed on IDEAS

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    1. Rong Zhang & Celso Santos & Madalena Moreira & Paula Freire & João Corte-Real, 2013. "Automatic Calibration of the SHETRAN Hydrological Modelling System Using MSCE," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4053-4068, September.
    2. Ye Tian & Yue-Ping Xu & Xu-Jie Zhang, 2013. "Assessment of Climate Change Impacts on River High Flows through Comparative Use of GR4J, HBV and Xinanjiang Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2871-2888, June.
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    Cited by:

    1. Kouao Laurent Kouadio & Jianxin Liu & Serge Kouamelan Kouamelan & Rong Liu, 2023. "Ensemble Learning Paradigms for Flow Rate Prediction Boosting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4413-4431, September.

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