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Inclusive hyper- to dilute-concentrated suspended sediment transport study using modified rouse model: parametrized power-linear coupled approach using machine learningKumar, S., Singh, H.P., Balaji, S., Hanmaiahgari, P.R., Pu, Jaan H. 31 July 2022 (has links)
Yes / 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.
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