Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/71126
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dc.contributor.authorCho, Siqi
dc.date.accessioned2017-05-15T05:09:13Z
dc.date.available2017-05-15T05:09:13Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/71126
dc.description.abstractMachine learning techniques have been widely used in various industries to perform forecasting and predictions. One such application that is covered in this project is trajectory prediction of maritime vessels. Information on vessels, such as International Maritime Organisation (IMO) ship identification number, its position, speed over ground (SOG) and course over ground (COG) etc. are broadcasted via automatic identification system (AIS). Making use of the availability of historical AIS data, various machine learning techniques can be applied to make future trajectory prediction based on the vessel’s current motion. The predictions made can be then visualised onto a map for users such as ship captains at sea, and port managements on land to plan the path of the vessel sailing in the port areas. The use of prediction can also be able to spot anomalous vessel’s trajectory which might lead to collision. This could improve port management, traffic efficiency around port and reduce incidents of vessels collision. With safety in mind, bring about this project to design a system, comprises of multiple sub-systems to make prediction using various machine learning algorithms, utilising the predictions made to visualise onto a map.en_US
dc.format.extent33 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titlePredict marine vessels’ trajectory with machine learning methodsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorHuang Guangbinen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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