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An efficient data driven-based model for prediction of the total sediment load in rivers

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Noori, Roohollah, Ghiasi, Behzad, Salehi, Sohrab, Esmaeili Bidhendi, Mehdi, Raeisi, Amin, Partani, Sadegh, Meysami, Rojin, Mahdian, Mehran, Hosseinzadeh, Majid and Abolfathi, Soroush (2022) An efficient data driven-based model for prediction of the total sediment load in rivers. Hydrology, 9 (2). 36. doi:10.3390/hydrology9020036

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Official URL: https://doi.org/10.3390/hydrology9020036

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Abstract

Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ecological functions of rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) and support vector regression (SVR) models modified by principal component analysis (PCA) to predict the TSL in rivers. A database of sediment measurement from large-scale physical modelling tests with 4759 datapoints were used to develop the predictive model. A dimensional analysis was performed based on the literature, and ten dimensionless parameters were identified as the key drivers of the TSL in rivers. These drivers were converted to uncorrelated principal components to feed the MLR and SVR models (PCA-based MLR and PCA-based SVR models) developed within this study. A stepwise PCA-based MLR and a 10-fold PCA-based SVR model with different kernel-type functions were tuned to derive an accurate TSL predictive model. Our findings suggest that the PCA-based SVR model with the kernel-type radial basis function has the best predictive performance in terms of statistical error measures including the root-mean-square error normalized with the standard deviation (RMSE/StD) and the Nash–Sutcliffe coefficient of efficiency (NSE), for the estimation of the TSL in rivers. The PCA-based MLR and PCA-based SVR models, with an overall RMSE/StD of 0.45 and 0.35, respectively, outperform the existing well-established empirical formulae for TSL estimation. The analysis of the results confirms the robustness of the proposed PCA-based SVR model for prediction of the cases with high concentration of sediments (NSE = 0.68), where the existing sediment estimation models usually have poor performance.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TC Hydraulic engineering. Ocean engineering
T Technology > TD Environmental technology. Sanitary engineering
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Sediment transport , Sediment transport -- Mathematical models , Dimensional analysis , Support vector machines, Kernel functions, Principal components analysis
Journal or Publication Title: Hydrology
Publisher: MDPI Publishing
ISSN: 2306-5338
Official Date: 17 February 2022
Dates:
DateEvent
17 February 2022Published
14 February 2022Accepted
Volume: 9
Number: 2
Article Number: 36
DOI: 10.3390/hydrology9020036
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access

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