Deep learning innovations in South Korean maritime navigation: Enhancing vessel trajectories prediction with AIS data.

ABSTRACT: 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 (CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Our research comprises two main parts: the first involves preprocessing the large raw AIS dataset to extract features, and the second focuses on trajectory prediction. We emphasize a specialized preprocessing approach tailored for AIS data, including advanced filtering techniques to remove outliers and erroneous data points, and the incorporation of contextual information such as environmental conditions and ship-speci

SUBMITTER: Zaman U 

PROVIDER: S-EPMC11500964 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature