Date of Award
Fall 12-2016
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Dr. Mahdi Abdelguerfi
Second Advisor
Dr. Md Tamjidul Hoque
Third Advisor
Dr. Thomas Soniat
Fourth Advisor
Dr. Christopher Summa
Abstract
A support vector machine (SVM) classifier was designed to replace a previous classifier which predicted oyster vessel behavior in the public oyster grounds of Louisiana. The SVM classifier predicts vessel behavior (docked, poling, fishing, or traveling) based on each vessel’s speed and either net speed or movement angle. The data from these vessels was recorded by a Vessel Monitoring System (VMS), and stored in a PostgreSQL database. The SVM classifier was written in Python, using the scikit-learn library, and was trained by using predictions from the previous classifier. Several validation and parameter optimization techniques were used to improve the SVM classifier’s accuracy. The previous classifier could classify about 93% of points from July 2013 to August 2014, but the SVM classifier can classify about 99.7% of those points. This new classifier can easily be expanded with additional features to further improve its predictive capabilities.
Recommended Citation
Frey, Devin, "A Machine Learning Approach to Determine Oyster Vessel Behavior" (2016). University of New Orleans Theses and Dissertations. 2253.
https://scholarworks.uno.edu/td/2253
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.