# A Machine Learning Approach to Determine Oyster Vessel Behavior
## 1 Introduction  
The Louisiana Department of Wildlife and Fisheries (LDWF, Baton Rouge, LA, USA) manages recreational and commercial fishing in the inland and coastal waters of Louisiana. One of the most important species fished in these waters is the eastern oyster, _Crassostreat virginica_. From 1997 to 2012, Louisiana accounted for an average of 34% of the nation's oyster landings, as well as 55% of all oysters landed among the Gulf Coast states in 2012 [1]. Over 700 permits are issued each year, giving vessels access to state oyster grounds, which total 1.6 million acres. In 2012, vessels fishing in Louisiana's public oyster grounds were required by law to have a satellite communications system--a Vessel Monitoring System (VMS)--installed to monitor vessel activity in public grounds [2]. The monitors periodically record vessel latitude and longitude at a given timestamp. While the modules allowed the LDWF to monitor vessel locations in public waters, they do not specify which behavior (docked/anchored, poling, fishing, or traveling) the vessel is performing at the time of the ping. Several attempts have been made to use VMS data to understand fishing vessel behavior, for example by using speed as a single criterion [3, 4, 5]. Conversely, [PERSON] [6] predicted the behavior of a vessel by running comparisons of speed versus net speed of a vessel through if-then-else conditions or rule-based classification. Vessel instantaneous speed provides information on how quickly the vessel is moving and net speed (calculated over certain time interval) provides the average speed of the vessel at particular time. However, [PERSON] approach could not account for every ping. About, 12% of the points from the 2012-2013 fishing season and about seven percent of points from the 2013-2014 fishing season fell into uncertain ranges, which were designated as \"unknown behavior\". The rule-based classifier suffers from the problem of not being able to classify significant amount of points in the dataset. Thus, the purpose of this project was to automate the oyster vessel behavior determination task and to produce a robust classifier that would accurately predict vessel behavior from position-at-time data transmitted from the on-board VMS modules. The advantage of developing a machine learning based model is that it suggests a most likely category for the given samples instead of not being able to classify them. Additionally, an automated system to determine fishing effort can overcome more error prone methods, such as physical logging by the captain of a fishing vessel. By having accurate measurements of fishing effort, the LDWF will be better prepared to manage oysters and their habitat. For example, knowing what reefs are most frequently fished would allow the LDWF to close the grounds to fishing vessels, or add oyster cultech (substrate) to restore oyster reef habitat. Machine learning (ML) [7, 8, 9] is a computing technology which recognizes the pattern in the training data and applies the knowledge it learned from the training data to effectively make prediction on test data. ML models find effective class decision boundaries from the data without being explicitly programmed where to look. In addition, ML is popularized due to its ability to devise complex models and algorithms by mining the patterns and features within the data. This is unlike the rule-based classifier; where rule-based classifiers use a deterministic approach to exhaustively map each data-point via if-then-else conditions. In this work, we propose a machine learning based automated approach that applies SVM with RBF kernel to determine the behavior of the oyster vessel from its trajectory data (see supplementary materials for code and data). The trajectory data is segmented into paths based on the speed at points calculated from the vessel ID, latitude, longitude and timestamp. Subsequently, net speed of each point in the path is obtained using a sliding window of ten points.