DP-BICNN: A Bidirectional Information Compensation Neural Network Coupled With Data-Driven and Physical Information for Sea Surface Temperature Prediction

Accurate prediction of sea surface temperature (SST) plays a crucial role in climate research, resource development, marine disaster prevention, and environmental protection. Traditional numerical models, while demonstrating excellent predictive accuracy, heavily rely on precise initial and boundary conditions. In contrast, data-driven approaches compensate for these shortcomings with their flexibility and lower computational costs; however, the lack of physical constraints and interpretability limits their effectiveness. Therefore, enhancing model efficiency and interpretability while ensuring accuracy has become a key challenge in current research. This article proposes a coupled dual-stream SST prediction model that integrates numerical simulation and data-driven methods to improve predictive accuracy and physical consistency. The model includes a bidirectional information compensation module, a physical equation constraint module, and an anomaly compensation module. First, the bidirectional information compensation module constructs physical information flow and complex process flow using Conv-LSTM networks and designs an interaction mechanism between the two streams to enhance the model’s ability to capture complex processes that cannot be explained by partial differential equations (PDEs). Next, the physical equation constraint module employs data assimilation techniques based on PDEs to simulate the dynamics of energy transfer in fluids, ensuring that the prediction results adhere to physical laws. Finally, the anomaly compensation module integrates the prediction results of physical processes and complex processes to output the final SST prediction. Experimental results demonstrate that compared to existing methods, this model achieves superior predictive accuracy across multiple datasets.