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 m3/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.
Highlights
River discharge and water level observation is an essential issue in hydrological and hydraulic modeling; in addition, it represents a piece of vital source information for water resources planning and management
We investigate the application of some new data-driven models to examine the stage-discharge relationship of some real datasets by using wavelet-based artificial neural networks (WANN), Support Vector Machine (SVM)-LF, Sustainability 2020, 12, 7877 and SVM-RF
The study site is the mainstream of the Burhabalang river which is an east-flowing river and a part of the Subarnarekha river basin located in Orissa State
Summary
River discharge and water level observation is an essential issue in hydrological and hydraulic modeling; in addition, it represents a piece of vital source information for water resources planning and management. Accurate stage-discharge estimation is crucial for estimating design flows for different hydraulic infrastructures, such as bridges, culverts, and canals [1]. In very dynamic or compound rivers, direct measurements of flow discharge are very often difficult or not feasible [2]. In some cases, neither discharge nor water level may be available or have the same data series record. In such circumstances, flow rating curves (FRCs) are the standard and most common procedure to estimate missing information regarding a specific variable. FRCs are most often used for medium and large rivers where making direct measurements may be costly in time and resources [4]
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