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Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India

Author

Listed:
  • Manish Kumar

    (Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture & Technology, Pantnagar 263145, India)

  • Anuradha Kumari

    (Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture & Technology, Pantnagar 263145, India)

  • Daniel Prakash Kushwaha

    (Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture & Technology, Pantnagar 263145, India)

  • Pravendra Kumar

    (Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture & Technology, Pantnagar 263145, India)

  • Anurag Malik

    (Punjab Agricultural University, Regional Research Station, Bathinda 151001, India)

  • Rawshan Ali

    (Department of Petroleum, Koya Technical Institute, Erbil Polytechnic University, Erbil 44001, Iraq)

  • Alban Kuriqi

    (CERIS, Instituto Superior Técnico, University of Lisbon, 1649-004 Lisbon, Portugal)

Abstract

Modeling the stage-discharge relationship in river flow is crucial in controlling floods, planning sustainable development, managing water resources and economic development, and sustaining the ecosystem. In the present study, two data-driven techniques, namely wavelet-based artificial neural networks (WANN) and a support vector machine with linear and radial basis kernel functions (SVM-LF and SVM-RF), were employed for daily discharge ( Q ) estimation. The hydrological data of daily stage ( H ) and discharge ( Q ) from June to October for 10 years (2004–2013) at the Govindpur station, situated in the Burhabalang river basin, Orissa, were considered for analysis. For model construction, an optimum number of inputs (lags) was extracted using the partial autocorrelation function (PACF) at a 5% level of significance. The outcomes of the WANN, SVM-LF, and SVM-RF models were appraised over the observed value of Q based on performance indicators, viz., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Pearson’s correlation coefficient (PCC), and Willmott index (WI), and through visual inspection (time variation, scatter plot, and Taylor diagram). Results of the evaluation showed that the SVM-RF model (RMSE = 104.426 m 3 /s, NSE = 0.925, PCC = 0.964, WI = 0.979 ) outperformed the WANN and SVM-LF models with the combination of three inputs, i.e., current stage, one-day antecedent stage, and discharge, during the testing period. In addition, the SVM-RF model was found to be more reliable and robust than the other models and having important implications for water resources management at the study site.

Suggested Citation

  • Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7877-:d:418082
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    References listed on IDEAS

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    1. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.

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