In the field of water process engineering, accurately predicting the total nitrogen (TN) concentration distribution in the Semi-Enclosed Bay area is of great importance for water quality assessment, pollution control, and scientific management. Due to the coupling of multiple influencing factors, the pollution process is complex, and traditional monitoring methods struggle to achieve large-scale, long-term real-time observation. Although numerical simulations can reproduce TN transport processes, they are computationally expensive and have low prediction efficiency. To address this, this study develops a deep learning hybrid model that integrates a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) network, referred to as the TCN-LSTM-Hybrid Model, to predict the spatiotemporal distribution of TN concentration fields in Shenzhen Bay. Comparative experiments show that this model outperforms traditional models such as TCN, LSTM, GRU, and MLP in terms of prediction accuracy and spatial generalization, offering higher computational efficiency and breaking through the limitations of “point-based prediction” by achieving “field-based prediction,” thereby providing a new path for pollutant simulation in complex ocean environments, supporting more informed decision making in ocean and coastal management.

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.