A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers
Abstract Sediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended‐to‐total load fraction using d...
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2022WR034401 |
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| author | Hyoseob Noh Yong Sung Park Il Won Seo |
| author_facet | Hyoseob Noh Yong Sung Park Il Won Seo |
| author_sort | Hyoseob Noh |
| collection | DOAJ |
| description | Abstract Sediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended‐to‐total load fraction using dimensionless hydro‐morphological variables. Two prominent variable combinations were identified using the recursive feature elimination for support vector regression (SVR): (a) width‐to‐depth ratio, dimensionless particle size, flow Reynolds number, densimetric Froude number, and falling particle Reynolds number, and (b) flow Reynolds number, Froude number, and densimetric Froude number. The explicit relations between the suspended‐to‐total load fraction and the two combinations were revealed by two modern symbolic regression methods: multi‐gene genetic programming and Operon. The five‐variable SVR model showed the best performance. Clustering analyses using a self‐organizing map and Gaussian mixture model, respectively, identified the underlying relationships between dimensionless variables. Subsequently, the one‐at‐a‐time sensitivity of the input variables of the empirical models was investigated. The suspended‐to‐total load fraction is positively related to the flow Reynolds number and is inversely related to the densimetric Froude number. The models developed in this study are practical and easy to implement in other suspended sediment monitoring methods because they only require basic measurable hydro‐morphological variables, such as velocity, depth, width, and median bed material size. |
| format | Article |
| id | doaj-art-a06f1247bfd2440c8d69dfa8f3fa25e7 |
| institution | Matheson Library |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-a06f1247bfd2440c8d69dfa8f3fa25e72025-06-27T07:44:26ZengWileyWater Resources Research0043-13971944-79732023-10-015910n/an/a10.1029/2022WR034401A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural RiversHyoseob Noh0Yong Sung Park1Il Won Seo2Department of Civil and Environmental Engineering Seoul National University Seoul South KoreaDepartment of Civil and Environmental Engineering Seoul National University Seoul South KoreaInstitute of Construction and Environmental Engineering Seoul National University Seoul South KoreaAbstract Sediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended‐to‐total load fraction using dimensionless hydro‐morphological variables. Two prominent variable combinations were identified using the recursive feature elimination for support vector regression (SVR): (a) width‐to‐depth ratio, dimensionless particle size, flow Reynolds number, densimetric Froude number, and falling particle Reynolds number, and (b) flow Reynolds number, Froude number, and densimetric Froude number. The explicit relations between the suspended‐to‐total load fraction and the two combinations were revealed by two modern symbolic regression methods: multi‐gene genetic programming and Operon. The five‐variable SVR model showed the best performance. Clustering analyses using a self‐organizing map and Gaussian mixture model, respectively, identified the underlying relationships between dimensionless variables. Subsequently, the one‐at‐a‐time sensitivity of the input variables of the empirical models was investigated. The suspended‐to‐total load fraction is positively related to the flow Reynolds number and is inversely related to the densimetric Froude number. The models developed in this study are practical and easy to implement in other suspended sediment monitoring methods because they only require basic measurable hydro‐morphological variables, such as velocity, depth, width, and median bed material size.https://doi.org/10.1029/2022WR034401sediment transportsuspended loadbed loadmachine learningsupport vector regressionclustering |
| spellingShingle | Hyoseob Noh Yong Sung Park Il Won Seo A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers Water Resources Research sediment transport suspended load bed load machine learning support vector regression clustering |
| title | A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers |
| title_full | A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers |
| title_fullStr | A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers |
| title_full_unstemmed | A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers |
| title_short | A Novel Efficient Method of Estimating Suspended‐To‐Total Sediment Load Fraction in Natural Rivers |
| title_sort | novel efficient method of estimating suspended to total sediment load fraction in natural rivers |
| topic | sediment transport suspended load bed load machine learning support vector regression clustering |
| url | https://doi.org/10.1029/2022WR034401 |
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