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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 ISSN 2306-5338.
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WRAP-Efficient-data-driven-model-prediction-total-sediment-load-rivers-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (7Mb) | Preview |
Official URL: https://doi.org/10.3390/hydrology9020036
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 | ||||||
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| 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 |
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| 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: |
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| Volume: | 9 | ||||||
| Number: | 2 | ||||||
| Article Number: | 36 | ||||||
| DOI: | 10.3390/hydrology9020036 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 17 February 2022 | ||||||
| Date of first compliant Open Access: | 22 February 2022 |
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