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dc.contributor.advisorKoyak, Robert A.
dc.contributor.authorYoung, Brian L.
dc.dateJun-17
dc.date.accessioned2017-08-14T16:47:11Z
dc.date.available2017-08-14T16:47:11Z
dc.date.issued2017-06
dc.identifier.urihttp://hdl.handle.net/10945/55564
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractAnalysts and security experts seek automated algorithms to predict future behavior of vessels at sea based on Automated Identification System (AIS) data. This thesis seeks to accurately predict the future location of a vessel at sea based on cluster analysis of historical vessel trajectories using a random forest. Once similar trajectories have been clustered into a route, expected prediction error can be empirically estimated based on an independent validation data set not used during training, then applied to an independent test set to produce an expected prediction region with a user-defined level of expectation. Our results show that the prediction region contains the true interpolated future position at the expectation level set by the user, therefore producing a valid methodology for both estimating the future vessel location and for assessing anomalous vessel behavior.en_US
dc.description.urihttp://archive.org/details/predictingvessel1094555564
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titlePredicting vessel trajectories from AIS data using Ren_US
dc.typeThesisen_US
dc.contributor.secondreaderHuddleston, Samuel H.
dc.contributor.departmentOperations Research (OR)
dc.subject.authorAISen_US
dc.subject.authorpredicten_US
dc.subject.authorrouteen_US
dc.subject.authortrajectoryen_US
dc.subject.authorclusteren_US
dc.subject.authorneural networken_US
dc.subject.authormodelen_US
dc.subject.authorrandom foresten_US
dc.description.recognitionOutstanding Thesis
dc.description.serviceMajor, United States Army Reserveen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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