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Regional Analysis of Flow Duration Curves through Support Vector Regression

Author

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  • Mehdi Vafakhah

    (Tarbiat Modares University)

  • Saeid Khosrobeigi Bozchaloei

    (Tarbiat Modares University)

Abstract

A flow-duration curve (FDC) shows the relationship between magnitude and frequency of daily streamflows over a specific time period. Artificial intelligence methods e.g. Support Vector Machines for Regression (SVR) and Artificial Neural Network (ANN) are useful techniques in the prediction of FDCs in ungagged basins. Regional analysis of FDCs were performed through SVR, ANN and Nonlinear Regression (NLR) using streamflow with durations of 0.02, 0.10, 0.20, 0.50 and 0.90% as dependent variables and six watershed characteristics chosen as effective independent variables on 33 selected watersheds in the Namak-Lake basin located in central zone of Iran. The results shows that the most important watershed characteristics are weighted average height, area, rangeland area, drainage density, permeable formation, and average stream slope. SVR has higher accuracy with relative root mean squared error (RMSEr) of 9.37 to 1.45 and Nash-Sutcliff criterion (NSE) of 0.54 to 0.91 than ANN with RMSEr with 9.42 to 3.79 and NSE of 0.39 to 0.86 and NLR with RMSEr with 18.04 to 3.38 and NSE of 0.53 to 0.79. In general, SVR is proposed to be used to estimate FDCs.

Suggested Citation

  • Mehdi Vafakhah & Saeid Khosrobeigi Bozchaloei, 2020. "Regional Analysis of Flow Duration Curves through Support Vector Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 283-294, January.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:1:d:10.1007_s11269-019-02445-y
    DOI: 10.1007/s11269-019-02445-y
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    References listed on IDEAS

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    1. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    2. Purna Nayak & Y. Rao & K. Sudheer, 2006. "Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network 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 77-90, February.
    3. Vahid Moosavi & Mehdi Vafakhah & Bagher Shirmohammadi & Negin Behnia, 2013. "A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1301-1321, March.
    4. Iman Fatehi & Bahman Amiri & Afshin Alizadeh & Jan Adamowski, 2015. "Modeling the Relationship between Catchment Attributes and In-stream Water Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5055-5072, November.
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    Cited by:

    1. Sabzekar, Mostafa & Hasheminejad, Seyed Mohammad Hossein, 2021. "Robust regression using support vector regressions," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    2. Xiaoming Guo & Lukai Xu & Lei Su & Yu Deng & Chaohui Yang, 2021. "Comparing Flow Duration Curves and Discharge Hydrographs to Assess Eco-flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4681-4693, November.

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