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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.
Lee et al. (Tue,) studied this question.