A Study of Correlation between Fishing Activity and AIS Data by Deep Learning

• Autorzy: Shen K. Y. , Chu Y. J. , Chang S. J. , Chang S. M.

Identyfikatory

DOI

10.12716/1001.14.03.01

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

AIS Data; deep learning framework; learning methods; recurrent neural network; (RNN); Automatic Identification System; (AIS); fishing operation

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2020
• Tom: Vol. 14. no. 3
• Strony: 527--531
• Opis fizyczny: Bibliogr. 12 poz., rys., tab.

Twórcy

autor

Shen K. Y.

• National Taiwan Ocean University, Keelung, Taiwan

autor

Chu Y. J.

• National Taiwan Ocean University, Keelung, Taiwan

autor

Chang S. J.

• National Taiwan Ocean University, Keelung, Taiwan

autor

Chang S. M.

• National Taiwan Ocean University, Keelung, Taiwan

Bibliografia

• [1] Flewwelling, P.,Cullinan, C., Balton, D., Sautter, R.P., Reynolds, J.E., "Recent trends in monitoring, control and surveillance systems for capture fisheries", FAO Fisheries Technical Paper. No. 415. Rome, FAO. 2002.
• [2] 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.
• [3] 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
• [4] 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.
• [5] 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.
• [6] 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.
• [7] 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.
• [8] 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.
• [9] 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.
• [10] 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.
• [11] S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780,1997.
• [12] 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.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020)