Date of Award
Summer 8-2021
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
School
Computing Sciences and Computer Engineering
Committee Chair
Dr. Dia Ali
Committee Chair School
Computing Sciences and Computer Engineering
Committee Member 2
Dr. A. Louise Perkins
Committee Member 2 School
Computing Sciences and Computer Engineering
Committee Member 3
Dr. John Harris
Committee Member 3 School
Mathematics and Natural Sciences
Committee Member 4
Dr. Bo Li
Committee Member 4 School
Computing Sciences and Computer Engineering
Committee Member 5
Dr. Brian S. Bourgeois
Abstract
Due to the difficulty and expense of collecting bathymetric data, modeling is the primary tool to produce detailed maps of the ocean floor. Current modeling practices typically utilize only one interpolator; the industry standard is splines-in-tension.
In this dissertation we introduce a new nominal-informed ensemble interpolator designed to improve modeling accuracy in regions of sparse data. The method is guided by a priori domain knowledge provided by artificially intelligent classifiers. We recast such geomorphological classifications, such as ‘seamount’ or ‘ridge’, as nominal data which we utilize as foundational shapes in an expanded ordinary least squares regression-based algorithm. To our knowledge we are the first to utilize the output of classifiers as input into a numerical model. This nominal information provides meta-knowledge about seafloor creation and growth into our models implicitly.
We performed two suites of experimental studies designed to clarify when these techniques add value. In our first study, we utilized the MergeBathy software for DBM construction to extensively investigate existing interpolators for feature-favoritism on different synthetic, idealized morphologies. This study reduced the possibility that the interpolators were a significant source of error in sparse data regions. Two feature-favoring interpolators then served as our nominal-informed interpolators and ensemble members. In our second study we utilized Friedman’s hypothesis testing to verify that our nominally informed ensemble method outperforms splines-in-tension in the presence of sparse data. To our knowledge this is the first comparison study of interpolation over sparse bathymetric data to verify statistically significant improvement in sparse-data regions.
Recommended Citation
Zambo, Samantha, "Ensemble Data Fitting For Bathymetric Models Informed by Nominal Data" (2021). Dissertations. 1897.
https://aquila.usm.edu/dissertations/1897
Included in
Computational Engineering Commons, Data Science Commons, Design of Experiments and Sample Surveys Commons, Geomorphology Commons, Numerical Analysis and Computation Commons, Numerical Analysis and Scientific Computing Commons, Oceanography Commons, Software Engineering Commons, Statistical Models Commons, Theory and Algorithms Commons