LSTM-Based Forecasting of Coastal Hypoxia in South Korea: Evaluating the Roles of Tide Level and Model Architecture

Forecasting coastal bottom dissolved oxygen (DO) concentrations is essential for hypoxia mitigation and ecosystem protection, however it remains challenging due to the complex interplay of physical and biogeochemical drivers. This study proposes a novel two-stage long short-term memory (LSTM) modeling framework for forecasting bottom DO in Gamak Bay, Korea—a semi-enclosed bay prone to frequent summer hypoxia. The two-stage framework separately forecasts bottom DO and other environmental variables, allowing the model to better focus on bottom DO while more effectively incorporating tide level predicted via harmonic decomposition. The model’s performance was evaluated across four configurations, considering the inclusion or exclusion of tide level as a predictor and comparing one-stage and two-stage LSTM architectures. Multi-year in situ hourly observations (2017–2023) and tide level calculated by harmonic decomposition were used for model training and evaluation. Results showed that incorporating tide level substantially improved long-term forecasting performance, especially when combined with the two-stage LSTM architecture. The two-stage LSTM with tide level achieved the highest accuracy for 120 h forecasts (RMSE = 1.6 mg/L). These findings highlight the critical role of tidal dynamics in hypoxia forecasting and offer guidance for improving hypoxia forecasting strategies in coastal environments.