Model performance for different ships.

<div><p>Predicting ship trajectories can effectively forecast navigation trends and enable the orderly management of ships, which holds immense significance for maritime traffic safety. This paper introduces a novel ship trajectory prediction method utilizing Convolutional Neural Network...

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Bibliographic Details
Main Author:	Umar Zaman (19859645) (author)
Other Authors:	Junaid Khan (4276162) (author), Eunkyu Lee (5967341) (author), Awatef Salim Balobaid (19859648) (author), R. Y. Aburasain (19859651) (author), Kyungsup Kim (19859654) (author)
Published:	2024
Subjects:	Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified recurrent neural network models &# 8212 method &# 8217 mean square error mean absolute error maritime traffic safety improvement effectively enhances holds immense significance gated recurrent unit first involves preprocessing experimental results reveal deep learning innovations deep learning algorithms average displacement error automatic identification system rnn ), gru lstm &# 8212 erroneous data points ade ), demonstrates ship trajectory prediction trajectory prediction lstm ), linear ade gru ). fde ), using metrics term memory substantially reduced specific characteristics second focuses robust foundation remove outliers promising advancements predict trajectories paper introduces orderly management next hour extract features environmental conditions contextual information comparative analysis also enriches
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Model performance for different ships.

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