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

Manish Kumar, Anuradha Kumari, Daniel Prakash Kushwaha, Pravendra Kumar, Anurag Malik, Rawshan Ali and Alban Kuriqi
Additional contact information
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

Sustainability, 2020, vol. 12, issue 19, 1-21

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.

Keywords: non-linear modeling; PACF; WANN; SVM-LF; SVM-RF; Govindpur (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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