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.
| Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-3363 |
| Date | 16 December 2016 |
| Creators | Frey, Devin |
| Publisher | ScholarWorks@UNO |
| Source Sets | University of New Orleans |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of New Orleans Theses and Dissertations |
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