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Predicting Sediment Load Using Stochastic Model and Rating Curves in a Hydrological Station
The linking of onsite rates of sedimentation and river water quality represent a major research need within the field of erosion, and it is considered to be an important scale problem in sediment yield prediction. Understanding sedimentation assessment and the sediment transport process is critical because it helps to estimate the amount of sediment moving through a river. Therefore, different methods are examined in this study to predict the suspended sediment load based on the collected data from various hydrometric stations. In order to calibrate and validate the modeling results, the suspended sediment load and flow data of the Abiaca Creek watershed over the period 1991–2003 are used. In addition, normal root mean square error, the Nash–Sutcliffe efficiency coefficient, the correlation coefficient, the Akaike information criterion (AIC), and the fitting between the observed and the predicted values of suspended sediment are calculated using a number of well-known statistical indexes. Furthermore, several stochastic methods with constant and variable coefficients, sediment rating curves, decomposition additives, and multiplicative are applied to predict the suspended sediment load. The results of this study show that stochastic modeling with variable coefficients has higher accuracy than the other methods in which the indicator of the normal root mean square error (NRMSE) is 0.27 and 0.37 for the simulation and prediction of the suspended sediment load, respectively.
Predicting Sediment Load Using Stochastic Model and Rating Curves in a Hydrological Station
The linking of onsite rates of sedimentation and river water quality represent a major research need within the field of erosion, and it is considered to be an important scale problem in sediment yield prediction. Understanding sedimentation assessment and the sediment transport process is critical because it helps to estimate the amount of sediment moving through a river. Therefore, different methods are examined in this study to predict the suspended sediment load based on the collected data from various hydrometric stations. In order to calibrate and validate the modeling results, the suspended sediment load and flow data of the Abiaca Creek watershed over the period 1991–2003 are used. In addition, normal root mean square error, the Nash–Sutcliffe efficiency coefficient, the correlation coefficient, the Akaike information criterion (AIC), and the fitting between the observed and the predicted values of suspended sediment are calculated using a number of well-known statistical indexes. Furthermore, several stochastic methods with constant and variable coefficients, sediment rating curves, decomposition additives, and multiplicative are applied to predict the suspended sediment load. The results of this study show that stochastic modeling with variable coefficients has higher accuracy than the other methods in which the indicator of the normal root mean square error (NRMSE) is 0.27 and 0.37 for the simulation and prediction of the suspended sediment load, respectively.
Predicting Sediment Load Using Stochastic Model and Rating Curves in a Hydrological Station
Azadi, Saeed (author) / Nozari, Hamed (author) / Godarzi, Ehsan (author)
2020-06-02
Article (Journal)
Electronic Resource
Unknown
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