Enhancing Multi-Output AIS Prediction with Indirect Sea Level Referencing: Feature Augmentation for Improved Accuracy in Korean Coastal Waters

Abstract

This study introduced a novel methodology for enhancing Automatic Identification System (AIS) trajectory forecasting in regions characterized by significant tidal variations through feature augmentation, specifically indirect incorporation of sea level data via the nearest tidal gauge. Traditional AIS prediction models predominantly utilize features such as latitude, longitude, speed over ground (SOG), and course over ground (COG) for time series forecasting. However, these models often overlook the influence of tidal fluctuations, which can significantly impact prediction accuracy in areas with pronounced tidal changes. To address this limitation, we proposed a feature augmentation approach by incorporating the Haversine distance to the nearest tidal gauge and the real-time sea level at that gauge as additional features. Direct access to sea level data at a vessel’s precise location presents practical challenges, making this indirect method an efficient and effective solution. Through comprehensive analyses across multiple deep learning models and test scenarios, our results demonstrate that this augmented feature set can substantially improve AIS forecasting performance in regions with significant tidal variation surrounding the Korean Peninsula.