# A Machine Learning Approach to Determine Oyster Vessel Behavior
## 6 Discussion and Conclusions  
The main objective of this work was to develop a machine learning based automated approach to determine the oyster vessel behavior. Towards this goal, we collected oyster vessel trajectory data and extracted two effective features--speed and net speed--to learn the ML-based method. We developed a machine learning method based on SVM with RBF kernel for multi-class classification of oyster vessel behavior into four different categories: fishing, traveling, poling (exploring) and docked (anchored). SVM uses maximum-margin affine hyperplanes to separate data into distinct groups. It penalizes the instances on the wrong side of the decision boundary using a cost parameter, C. Furthermore, the RBF kernel contains an attribute, gamma, which affects the area of influence of each training instance. Both \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-score** \\ \hline
**Docked** & 1.00 & 1.00 & 1.00 \\
**Fishing** & 1.00 & 1.00 & 1.00 \\
**Travel** & 1.00 & 1.00 & 1.00 \\
**Poling** & 1.00 & 1.00 & 1.00 \\
**Average** & 1.00 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results obtained for precision, recall and F1-score from independent test. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Docked** & **Fishing** & **Travel** & **Poling** \\ \hline
**Docked** & 706 & 0 & 0 & 0 \\
**Fishing** & 0 & 2929 & 0 & 0 \\
**Travel** & 0 & 0 & 27,259 & 0 \\
**Poling** & 0 & 2 & 0 & 523 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results obtained for confusion matrix from independent test. the parameters C and \(\gamma\) were optimized using a grid search algorithm. We found that the SVM with RBF kernel provides a 10-fold CV accuracy of 99.99% in the prediction of oyster vessel behavior on the validation set. Furthermore, our predictor attains a precision of 1.00, recall of 1.00, \(F1\)-score of 1.00 and a test accuracy of 99.99%, when tested on the independent test set. Oyster fishing in Louisiana is generally done by dredging over subtidal oyster reefs. Classifiers distinguishing fishing from other types of vessel behaviors could be used to discover oyster reefs that were unknown to the LDWF. By using data from vessel's fishing, poling and movement patterns, one could thus locate reefs that were missed by the previous traditional Side Scan Sonar methods used by the LDWF, as well as determine the most utilized areas of each reef. With greater understanding of fishing behavior over shorter periods of time, one could determine catch per unit effort more precisely--e.g., in sacks per hour fished as opposed to sacks per day. There are many additions and adjustments that could improve the management of VMS data and the classification of vessel behavior. Adding a graphical user interface to the tools would make them easier to use. Using additional nautical data, such as location of docks, waterways and oyster reefs, would provide a spatial context. If more parameters are used to determine vessel behavior, updating the classifiers to use them would be easy. The inclusion of more parameters would increase training time, but it would likely improve accuracy. In the present study, speed and direction as criteria for vessel behavior, were determined by limited onboard observations and interviews with oyster fishermen. Additional ground truth information that links a timestamp to a vessel behavior observed in situ would provide an improved training data set. The code and data used to predict vessel behavior is available here: [[http://cs.uno.edu/~tamjid/Software/DVB/Code_Data.zip](http://cs.uno.edu/~tamjid/Software/DVB/Code_Data.zip)]([http://cs.uno.edu/~tamjid/Software/DVB/Code_Data.zip](http://cs.uno.edu/~tamjid/Software/DVB/Code_Data.zip)). We gratefully acknowledge the financial support of the Louisiana Department of Wildlife and Fisheries. [PERSON] and [PERSON] provided helpful information on fishing practices. [PERSON] also provided boat time for a ground truth study on his vessel, _Croatian Pride_.