Inclusive Hyper- to Dilute-Concentrated Suspended Sediment Transport Study Using Modified Rouse Model: Parametrized Power-Linear Coupled Approach Using Machine Learning

The transfer of suspended sediment can range widely from being diluted to hyper-concentrated, depending on the local flow and ground conditions. Using Rouse model Kundu Ghoshal (2017) model, it is possible look at distribution for a hyper-concentrated flows. According follows linear profile regime power law applies dilute concentrated regime. This paper describes these models how parameters (linear-law coefficients power-law coefficients) are dependent using machine-learning techniques. used XGboost Classifier, Linear Regressor (Ridge), (Bayesian), K Nearest Neighbours, Decision Tree Regressor, Support Vector Machines (Regressor). were implemented Google Colab have been applied determine relationship between every parameter with each (mean concentration, number, size parameter) both profile. correctly calculated conditions (0.268 mm≤d50≤2.29 mm, 0.00105gmm3≤particle density≤2.65gmm3, 0.197mms≤vs≤96mms, 7.16mms≤u*≤63.3mms, 0.00042≤cˉ≤0.54), including numbers (0.0076≤P≤23.5). showed particularly good accuracy testing low extremely high concentrations type I III profiles.