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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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    Cited by:

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