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    Inclusive hyper- to dilute-concentrated suspended sediment transport study using modified rouse model: parametrized power-linear coupled approach using machine learning

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    Pu_et_al_Fluids (630.3Kb)
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    Publication date
    2022-07
    Author
    Kumar, S.
    Singh, H.P.
    Balaji, S.
    Hanmaiahgari, P.R.
    Pu, Jaan H.
    Keyword
    Rouse number
    Mean concentration
    Suspended sediment transport
    Sediment size parameter
    Parameterized power-linear model
    Machine learning
    Decision tree regressor
    Support vector machines
    Rights
    © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
    Metadata
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    Abstract
    The transfer of suspended sediment can range widely from being diluted to being hyperconcentrated, depending on the local flow and ground conditions. Using the Rouse model and the Kundu and Ghoshal (2017) model, it is possible to look at the sediment distribution for a range of hyper-concentrated and diluted flows. According to the Kundu and Ghoshal model, the sediment flow follows a linear profile for the hyper-concentrated flow regime and a power law applies for the dilute concentrated flow regime. This paper describes these models and how the Kundu and Ghoshal parameters (linear-law coefficients and power-law coefficients) are dependent on sediment flow parameters using machine-learning techniques. The machine-learning models used are XGboost Classifier, Linear Regressor (Ridge), Linear Regressor (Bayesian), K Nearest Neighbours, Decision Tree Regressor, and Support Vector Machines (Regressor). The models were implemented on Google Colab and the models have been applied to determine the relationship between every Kundu and Ghoshal parameter with each sediment flow parameter (mean concentration, Rouse number, and size parameter) for both a linear profile and a power-law profile. The models correctly calculated the suspended sediment profile for a range of flow conditions ( 0.268 𝑚𝑚𝑚𝑚 ≤ 𝑑𝑑50 ≤ 2.29 𝑚𝑚𝑚𝑚, 0.00105 𝑔𝑔 𝑚𝑚𝑚𝑚3 ≤ particle density ≤ 2.65 𝑔𝑔 𝑚𝑚𝑚𝑚3 , 0.197 𝑚𝑚𝑚𝑚 𝑠𝑠 ≤ 𝑣𝑣𝑠𝑠 ≤ 96 𝑚𝑚𝑚𝑚 𝑠𝑠 , 7.16 𝑚𝑚𝑚𝑚 𝑠𝑠 ≤ 𝑢𝑢∗ ≤ 63.3 𝑚𝑚𝑚𝑚 𝑠𝑠 , 0.00042 ≤ 𝑐𝑐̅≤ 0.54), including a range of Rouse numbers (0.0076 ≤ 𝑃𝑃 ≤ 23.5). The models showed particularly good accuracy for testing at low and extremely high concentrations for type I to III profiles.
    URI
    http://hdl.handle.net/10454/19095
    Version
    Published version
    Citation
    Kumar S, Singh HP, Balaji S et al (2022) Inclusive hyper- to dilute-concentrated suspended sediment transport study using modified rouse model: parametrized power-linear coupled approach using machine learning. Fluids. 7(8): 261.
    Link to publisher’s version
    https://doi.org/10.3390/fluids7080261
    Type
    Article
    Collections
    Engineering and Informatics Publications

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