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2022 Journal Impact Factor - 0.6
2022 CiteScore - 1.7
ISSN 2083-6473
ISSN 2083-6481 (electronic version)
Editor-in-Chief
Associate Editor
Prof. Tomasz Neumann
Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
e-mail transnav@umg.edu.pl
A Study of Correlation between Fishing Activity and AIS Data by Deep Learning
Times cited (SCOPUS): 4
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.
KEYWORDS: Automatic Identification System (AIS), AIS Data, Fishing, Deep Learning Framework, Learning Methods, Deep Learning, Recurrent Neural Network (RNN), Fishing Operation
REFERENCES
Matthew J. Witt and Brendan J. Godley, “A step towards seascape scale conservation: using vessel monitoring systems (VMS) to map fishing activity,” PLoS One, 2(10), e1111, 2007. - doi:10.1371/journal.pone.0001111
S.J. Chang, “Satellite-based vessel tracking and monitoring as the long-range mode of AIS,” Proceedings of MTS/IEEE Oceans 2005 Conference, Washington D.C., USA, 2005.
Rijnsdorp, A., Buys, A., Storbeck, F., and Visser, E, “Micro-scale distribution of beam trawl effort in the southern North Sea between 1993 and 1996 in relation to the trawling frequency of the sea bed and the impact on benthic organisms,” ICES Journal of Marine Science: Journal du Conseil, 55(3), 403-419, 1998. - doi:10.1006/jmsc.1997.0326
Joo, R., Bertrand, S., Chaigneau, A., and Niquen, M,“Optimization of an artificial neural network for identifying fishing set positions from VMS data: an example from the Peruvian anchovy purse seine fishery,” Ecological Modelling, 222(4), 1048-1059, 2011. - doi:10.1016/j.ecolmodel.2010.08.039
Russo, T., Parisi, A., Prorgi, M., Boccoli, F., Cignini, I., Tordoni, M., and Cataudella, S.,“When behaviour reveals activity: Assigning fishing effort to métiers based on VMS data using artificial neural networks,” Fisheries Research, 111(1–2), 53-64, 2011. - doi:10.1016/j.fishres.2011.06.011
S.J. Chang, K.H. Yeh, G.D. Peng, S.M. Chang and C.H. Huang, “From Safety to Security- pattern and anomaly detections in maritime trajectories,” Proceedings of the 49th Annual International Carnahan Conference on Security Technology, ICCST 2015. - doi:10.1109/CCST.2015.7389720
E. N. de Souza, K. Boerder, S. Matwin, and B. Worm, “Improving fishing pattern detection from satellite ais using data mining and machine learning,” PLOS ONE, vol. 11, no. 7, p. e0158248, 2016. - doi:10.1371/journal.pone.0158248
Baifan Hu, Xiang Jiang, and Stan Matwin, “Identifying Fishing Activities from AIS Data with Conditional Random Fields,” Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2016, pp. 1–7.
X. Jiang, D. L. Silver, B. Hu, E. N. de Souza, and S. Matwin, “Fishing activity detection from ais data using autoencoders,” in Canadian Conference on Artificial Intelligence. Springer, 2016, pp. 33–39. - doi:10.1007/978-3-319-34111-8_4
S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780,1997. - doi:10.1162/neco.1997.9.8.1735
S.J Chang, “AIS Applications as an Efficient Tool for VTS: Identifying and Coping with Discrepancy between Ideal Cases, Standard and Real Situations,” Sea Technology 47(3): 15-18, 2006.
Citation note:
Shen K.Y., Chu Y.J., Chang S.J., Chang S.M.: A Study of Correlation between Fishing Activity and AIS Data by Deep Learning. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 14, No. 3, doi:10.12716/1001.14.03.01, pp. 527-531, 2020
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