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Article

Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation

1
Department of Watershed Management Engineering, Sari Agricultural Sciences and Natural Resources University, Sari 4818168984, Iran
2
Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
3
Mountain Societies Research Institute, University of Central Asia, Khorog 736000, Tajikistan
*
Author to whom correspondence should be addressed.
Academic Editors: Bommanna Krishnappan and Achim A. Beylich
Water 2021, 13(24), 3650; https://doi.org/10.3390/w13243650
Received: 23 November 2021 / Revised: 12 December 2021 / Accepted: 16 December 2021 / Published: 19 December 2021
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. View Full-Text
Keywords: suspended sediment modeling; metaheuristic algorithm; sediment rating curve; support vector regression; data separation suspended sediment modeling; metaheuristic algorithm; sediment rating curve; support vector regression; data separation
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MDPI and ACS Style

Asadi, H.; Dastorani, M.T.; Sidle, R.C.; Shahedi, K. Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation. Water 2021, 13, 3650. https://doi.org/10.3390/w13243650

AMA Style

Asadi H, Dastorani MT, Sidle RC, Shahedi K. Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation. Water. 2021; 13(24):3650. https://doi.org/10.3390/w13243650

Chicago/Turabian Style

Asadi, Haniyeh, Mohammad T. Dastorani, Roy C. Sidle, and Kaka Shahedi. 2021. "Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation" Water 13, no. 24: 3650. https://doi.org/10.3390/w13243650

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