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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 …