Physics-Guided AI Tide Forecasting with Nodal Modulation: A Multi-Station Study in South Korea

Seung-Jun Lee, Tae-Yun Kim, Soo-Gil Lee, Ji-Sung Kim and Hong-Sik Yun ()
Additional contact information
Seung-Jun Lee: Geodesy Laboratory, Civil & Architectural and Environmental System Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea
Tae-Yun Kim: Geodesy Laboratory, Civil & Architectural and Environmental System Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea
Soo-Gil Lee: Disaster & Risk Management Laboratory, Interdisciplinary Program in Crisis & Disaster and Risk Management, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea
Ji-Sung Kim: School of Geography, Faculty of Environment, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK
Hong-Sik Yun: Geodesy Laboratory, Civil & Architectural and Environmental System Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea

Sustainability, 2025, vol. 17, issue 21, 1-30

Abstract: Tidal prediction is essential for navigation safety, coastal risk management, and climate adaptation. This study develops and validates a hybrid harmonic analysis–artificial intelligence (HA–AI) framework to improve decadal tidal forecasting at five tide gauge stations along the Korean coast. Using ten years of hourly sea-level observations (2015–2025), harmonic decomposition captures deterministic astronomical components, while station-specific long short-term memory (LSTM) models learn residual nonlinear dynamics. Validation against the independent 2025 dataset demonstrates substantial accuracy gains compared with harmonic analysis alone. Across all stations, the hybrid approach reduced root mean square error (RMSE) by 16–40% (average 32.3%), with RMSE values of 8.1–10.8 cm, mean absolute errors (MAEs) of 6.3–8.9 cm, and correlation coefficients (R) ranging from 0.76 to 0.96. At Busan, RMSE was reduced from 15.1 cm (HA) to 9.9 cm (hybrid), while at Sokcho, improvement reached 40.1%. Uncertainty analysis further confirmed reliability, with 46.2% of residuals contained within ±2σ bounds. These results highlight the hybrid framework’s ability to integrate physical interpretability with adaptive skill, ensuring robust and transferable forecasts across heterogeneous coastal settings. The findings provide practical value for navigation, flood preparedness, and climate-resilient coastal planning, and demonstrate the potential of hybrid models as an operational forecasting tool.

Keywords: coastal risk; decadal variability; harmonic analysis; hybrid modeling; nodal modulation; residual learning; sea-level rise; tide gauges; tidal forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/21/9579/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/21/9579/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:21:p:9579-:d:1781614

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().