Spatiotemporal Modeling of Dissolved Oxygen in a Semi-Enclosed Water Body with a LSTM-GRU Hybrid Approach

The dynamic evolution of dissolved oxygen (DO) concentration is critical for aquatic ecosystem stability and biodiversity, serving as an important water quality indicator. Predicting DO distribution is challenging due to complex hydrodynamic conditions and environmental disturbances. While traditional experimental methods provide accurate short-term data, they are limited in spatial coverage, costly, and lack real-time predictive capabilities. Computational fluid dynamics (CFD) simulations, though beneficial for mechanism modeling, suffer from high computational costs and reduced accuracy in long-term, nonlinear predictions. This study addresses these limitations by developing an LSTM-GRU Hybrid Model for predicting DO concentration in Shenzhen Bay. Combining Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), the model enhances time-memory capabilities and parameter efficiency. Results show that the LSTM-GRU Hybrid Model outperforms single neural networks with a correlation coefficient close to 0.99 and RMSE below 0.04 gO2/m3. This study not only introduces a novel methodology for modeling dissolved oxygen in Shenzhen Bay, but also contributes to advancing predictive capabilities in earth systems environment and offers methodological insights applicable to similar semi-enclosed marine environments.