Abstract
Information on the transport of fluvial suspended sediment loads (SSL) is crucial due to its effects on water quality, pollutant transport and transformation, dam operations, and reservoir capacity. As such, adopting a reliable method to accurately estimate SSL is a key topic for watershed managers, hydrologists, river engineers, and hydraulic engineers. One of the most common methods for estimating SSL or suspended sediment concentrations (SSC) is sediment rating curve (SRC), which has several weaknesses. Here, we optimize the SRC equation using two main approaches. Firstly, three well recognized metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA)) were used together with two classical approaches (food and agriculture organization (FAO) and non-parametric smearing estimator (CF2)) to optimize the coefficients of the SRC regression model. The second approach uses separation of data based on season and flow discharge (Qw) characteristics. A support vector regression (SVR) model using only Qw as an input was employed for SSC estimation and the results were compared with the SRC and its optimized versions. Metaheuristic algorithms improved the performance of the SRC model and the PSO model outperformed the other algorithms. These results also indicate that the model performance was directly related to the temporal separation of data. Based on these findings, if data are more homogenous and related to the limited climatic conditions used in the estimation of SSC, the estimations are improved. Moreover, it was observed that optimizing SRC through metaheuristic models was much more effective than separating data in the SCR model. The results also indicated that with the same input data, SVR was superior to the SRC model and its optimized version.
Highlights
Having adequate up-to-date information about sediment loads in rivers is important for hydraulic, river engineering, and water resources projects [1,2]
The same condition was observed with RMSE, which minimized the square of residuals, while mean absolute error (MAE) was less sensitive to large values [79]
Our research examined and provided a reliable method that can accurately estimate suspended sediment concentrations (SSC) by: (1) optimizing Sediment rating curve (SRC) using metaheuristic algorithms (GA, particle swarm optimization (PSO), and imperialist competitive algorithm (ICA)) and classical approaches (FAO and CF2), (2) improving estimation power of SRC using separation of data based on season and flow discharge characteristics and (3) using the support vector regression (SVR) model with only flow discharge values as inputs, similar to the SRC approach
Summary
Having adequate up-to-date information about sediment loads in rivers is important for hydraulic, river engineering, and water resources projects [1,2]. A review of previous studies shows that, many researchers used indirect methods or alternative approaches for estimating of bed load [6,7,8,9] and suspended load [6,10] Most of these sediment transport functions require comprehensive information on the channel, flow conditions, and sediment characteristics [11]. Sediment rating curve (SRC) is the most common regression-based model which has been applied throughout the world in different environmental conditions to estimate suspended sediment in rivers [12,13,14,15,16,17,18,19,20] This method expresses the empirical relationship between suspended sediment loads (SSL) or suspended sediment concentrations (SSC) and flow discharge (Qw) by power, linear, or polynomial functions [13,19,20,21,22,23]
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