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Mach. Learn. Knowl. Extr. 2018, 1(1), 64-74; https://doi.org/10.3390/make1010004

A Machine Learning Approach to Determine Oyster Vessel Behavior

1
Canizaro/Livingston Gulf States Center for Environmental Informatics, University of New Orleans, New Orleans, LA 70148, USA
2
Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA
3
Department of Biological Sciences, University of New Orleans, New Orleans, LA 70148, USA
*
Author to whom correspondence should be addressed.
Received: 14 December 2017 / Revised: 20 March 2018 / Accepted: 29 March 2018 / Published: 31 March 2018
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Abstract

In this work, we address a multi-class classification task of oyster vessel behaviors determination by classifying them into four different classes: fishing, traveling, poling (exploring) and docked (anchored). The main purpose of this work is to automate the oyster vessel behaviors determination task using machine learning and to explore different techniques to improve the accuracy of the oyster vessel behavior prediction problem. To employ machine learning technique, two important descriptors: speed and net speed, are calculated from the trajectory data, recorded by a satellite communication system (Vessel Management System, VMS) attached to the vessels fishing on the public oyster grounds of Louisiana. We constructed a support vector machine (SVM) based method which employs Radial Basis Function (RBF) as a kernel to accurately predict the behavior of oyster vessels. Several validation and parameter optimization techniques were used to improve the accuracy of the SVM classifier. A total 93% of the trajectory data from a July 2013 to August 2014 dataset consisting of 612,700 samples for which the ground truth can be obtained using rule-based classifier is used for validation and independent testing of our method. The results show that the proposed SVM based method is able to correctly classify 99.99% of 612,700 samples using the 10-fold cross validation. Furthermore, we achieved a precision of 1.00, recall of 1.00, F1-score of 1.00 and a test accuracy of 99.99%, while performing an independent test using a subset of 93% of the dataset, which consists of 31,418 points. View Full-Text
Keywords: oyster vessel behavior; trajectory data; support vector machine; machine learning oyster vessel behavior; trajectory data; support vector machine; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Frey, D.J.; Mishra, A.; Hoque, M.T.; Abdelguerfi, M.; Soniat, T. A Machine Learning Approach to Determine Oyster Vessel Behavior. Mach. Learn. Knowl. Extr. 2018, 1, 64-74.

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