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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
| Content Provider | MDPI |
|---|---|
| Author | Kumar, Sanny Singh, Harendra Prasad Balaji, Srinivas Hanmaiahgari, Prashanth Reddy Pu, Jaan H. |
| Copyright Year | 2022 |
| Abstract | The transfer of suspended sediment can range widely from being diluted to being hyper-concentrated, 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 ( |
| Starting Page | 261 |
| e-ISSN | 23115521 |
| DOI | 10.3390/fluids7080261 |
| Journal | Fluids |
| Issue Number | 8 |
| Volume Number | 7 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-30 |
| Access Restriction | Open |
| Subject Keyword | Fluids Marine Engineering Rouse Number Mean Concentration Suspended Sediment Transport Sediment Size Parameter Parameterized Power-linear Model Machine Learning Decision Tree Regressor Support Vector Machines |
| Content Type | Text |
| Resource Type | Article |
