Date of Award
12-2019
Thesis Date
12-2019
Degree Type
Honors Thesis-Unrestricted
Degree Name
B.S.
Department
Computer Science
Degree Program
Computer Science
Director
Mahdi Abdelguerfi
Abstract
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.
Recommended Citation
Vu, Huy, "A Machine Learning Assessment to Predict the Sediment Transport Rate Under Oscillating Sheet Flow Conditions" (2019). Senior Honors Theses. 135.
https://scholarworks.uno.edu/honors_theses/135
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this honors thesis in whole or part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the honors thesis.