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

• 발행기관: 한국항해항만학회
• 저자명: Yoonseok Lee Hyunwoo Park Deukjae Cho Wonhee Lee
• 간행물 정보: 『한국항해항만학회지』제49권 제1호, 18~35쪽, 전체 18쪽
• 주제분류: 공학 > 해양공학
• 파일형태: PDF
• 발행일자: 2025.02.28

국문 초록

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.

목차

1. Introduction

2. Literature Review

3. Definitions and Problem Statements

4. Experiments and Discussion

5. Conclusion

Acknowledgements

Appendix

References

키워드

Automatic Identification System (AIS) time-series multi-output forecasting deep learning transformer feature augmentation sea level