| Abstrakt: |
The accurate forecasts and estimations of the amount of sediment transported by rivers are critical concerns in water resource management and soil and water conservation. The identification of appropriate and applicable models or improvements in existing approaches is needed to accurately estimate the suspended sediment concentration (SSC). In recent decades, the utilization of intelligent models has substantially improved SSC estimation. The identification of beneficial and proper input parameters can greatly improve the performance of these smart models. In this regard, we assessed the C-factor of the revised universal soil loss equation (RUSLE) as a new input along with hydrological variables for modeling SSC. Four data-driven models (feed-forward neural network (FFNN); support vector regression (SVR); adaptive neuro-fuzzy inference system (ANFIS); and radial basis function (RBF)) were applied in the Boostan Dam Watershed, Iran. The cross-correlation function (CCF) and partial autocorrelation function (PAFC) approaches were applied to determine the effective lag times of the flow rate and suspended sediment, respectively. Additionally, several input scenarios were constructed, and finally, the best input combination and model were identified through trial and error and standard statistics (coefficient of determination (R2); root mean square error (RMSE); mean absolute error (MAE); and Nash–Sutcliffe efficiency coefficient (NS)). Our findings revealed that using the C-factor can considerably improve model efficiency. The best input scenario in which the C-factor was combined with hydrological data improved the NS by 16.4%, 21.4%, 0.17.5%, and 23.2% for SVR, ANFIS, FFNN, and RBF models, respectively, compared with the models using only hydrological inputs. Additionally, a comparison among the different models showed that the SVR model had about 4.1%, 13.7%, and 23.3% (based on the NS metric) higher accuracy than ANFIS, FFNN, and RBF for SSC estimation, respectively. Thus, the SVR model using hydrological data along with the C-factor can be a cost-effective and promising tool in SSC prediction at the watershed scale. [ABSTRACT FROM AUTHOR] |
