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Full-Text Articles in Physical Sciences and Mathematics
Machine Learning Model Selection For Predicting Global Bathymetry, Nicholas P. Moran
Machine Learning Model Selection For Predicting Global Bathymetry, Nicholas P. Moran
University of New Orleans Theses and Dissertations
This work is concerned with the viability of Machine Learning (ML) in training models for predicting global bathymetry, and whether there is a best fit model for predicting that bathymetry. The desired result is an investigation of the ability for ML to be used in future prediction models and to experiment with multiple trained models to determine an optimum selection. Ocean features were aggregated from a set of external studies and placed into two minute spatial grids representing the earth's oceans. A set of regression models, classification models, and a novel classification model were then fit to this data and …
Forecasting The Development Of A Natural Levee Breach And Subsequent Sediment Distribution At Mardi Gras Pass, Louisiana, Joshua Hansen
Forecasting The Development Of A Natural Levee Breach And Subsequent Sediment Distribution At Mardi Gras Pass, Louisiana, Joshua Hansen
University of New Orleans Theses and Dissertations
In 2012, a breach in a natural levee occurred on the Mississippi River near the Bohemian Spillway, forming a new distributary named Mardi Gras Pass. Since its genesis, scientists from local universities and NGOs have been regularly performing bathymetric and bank surveys to track the channel’s expansion, as well as discharge surveys throughout the receiving basin. This study developed and implemented a hindcast simulation based on this wealth of data utilizing the morpho- and hydrodynamic model, Delft3D. This model was then used to create a 20-year forecast and a 1-year simulation without tidal and subtidal forces. The results demonstrated that …