@article{Shen_Chu_Chang_Chang_2020, author = {Shen, Kuan Yu and Chu, Ying Jui and Chang, Shwu Jing and Chang, Shih Ming}, title = {A Study of Correlation between Fishing Activity and AIS Data by Deep Learning}, journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation}, volume = {14}, number = {3}, pages = {527-531}, year = {2020}, url = {./Article_A_Study_of_Correlation_between_Shen,55,1031.html}, abstract = {Previous researches on the prediction of fishing activities mainly rely on the speed over ground (SOG) as the referential attribute to determine whether the vessel is navigating or in fishing operation. Since more and more fishing vessels install Automatic Identification System (AIS) either voluntarily or under regulatory requirement, data collected from AIS in real time provide more attributes than SOG which may be utilized to improve the prediction. To be specific, the ships' trajectory patterns and the changes in course become available and should be considered. This paper aims to improve the accuracy in the identification of fishing activities. First, we do feature extraction from the AIS data of coastal waters around Taiwan and build a Recurrent Neural Network (RNN) model. Then, the activity data of fishing vessels are divided into fishing and non-fishing. Finally, based on the testing by feeding various fishing activity data, we can identify the fishing status automatically.}, doi = {10.12716/1001.14.03.01}, issn = {2083-6473}, publisher = {Gdynia Maritime University, Faculty of Navigation}, keywords = {Automatic Identification System (AIS), AIS Data, Fishing, Deep Learning Framework, Learning Methods, Deep Learning, Recurrent Neural Network (RNN), Fishing Operation} }