Document Type : Original Article

Authors

• Ali Asghar Alesheikh ¹
• Saeed mehri ²

¹ Full Prof., Dep. of Geomatics Engineering, K.N. Toosi University of Technology, Tehran

² K. N. Toosi University of Technology

Abstract

About 80% of world transportation happens at sea. Therefore the safety of vessels, in particular
during vessels’ movement, is crucially important. As different contextual parameters affect vessels’
movement, selecting optimal contextual parameters is one of the main changes in vessels’ Context-
Aware movement analysis. Toward this end, a Long Short-Term Memory (LSTM) network is used
for wrapper feature selection to identify optimal contextual parameters for vessels’ movement
prediction. To do this, the Automatic Identification System (AIS) dataset from the eastern coast of the
United States of America collected from December 2017 is used. All possible combinations of three
contextual parameters, including speed, course and vessels’ presence probability in different positions
at sea, were evaluated using the wrapper method in the LSTM network. In all evaluations, 70% of
data was used for training and the remaining for cross-validation. The results selected speed and
presence probability as optimal contextual parameters for vessel movement prediction. The model
trained with optimal contextual parameters is 26.98% more accurate than a model trained with all
available contextual parameters and 16.14% better than a model without contextual parameters.
Therefore, selecting optimal parameters from available contextual parameters can help improve the
accuracy of vessels’ predictions. Keywords: Context-Aware, Long Short-Term Memory, Automatic
Identification System, wrapper, Movement prediction, Context.

Keywords

• Context-Aware
• Long Short-Term Memory
• Automatic Identification System
• wrapper
• Movement prediction
• Context