The two-phase flow approach has been the conventional method designed to study the sediment transport rate. Due to the complexity of sediment transport, the precisely numerical models computed from that approach require initial assumptions and, as a result, may not yield accurate output for all conditions. This research work proposes that Machine Learning algorithms can be an alternative way to predict the processes of sediment transport in two-dimensional directions under oscillating sheet flow conditions, by utilizing the available dataset of the SedFoam multidimensional two-phase model. The assessment utilized linear regression and gradient boosting algorithm to analyze the lowest average mean squared error in each case and search for the best partition method based on the domain height of the simulation setup.
| Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:honors_theses-1134 |
| Date | 01 December 2019 |
| Creators | Vu, Huy |
| Publisher | ScholarWorks@UNO |
| Source Sets | University of New Orleans |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Senior Honors Theses |
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