Spatiotemporal Prediction of Tidal Fields in a Semi-Enclosed Marine Bay Using Deep Learning

Abstract

The prediction of tidal fields is crucial in coastal and marine hydrodynamic analyses, particularly in complex tidal environments, as it plays an essential role in disaster warning and fisheries management. However, monitoring the entire tidal field is impractical, and harmonic analysis and numerical simulation methods continue to face challenges in accuracy and efficiency for large-scale predictions. To address these issues, this paper proposes a tidal field prediction method based on Long Short-Term Memory (LSTM) networks. A physics-based hydrodynamic model is established, and the numerical model is validated using observational data from multiple sites in the study area. The accuracy is quantified using performance indicators such as root mean square error (RMSE) and correlation coefficients. The validated numerical model is then used to generate a high-quality comprehensive dataset. An LSTM-based model is then developed to predict tidal fields in a semi-closed marine bay. The performance of the LSTM-based model is compared with models developed using Transformer, Random Forest, and KNN regression methods. The results demonstrate that the LSTM-based model surpasses the other machine learning models in prediction accuracy, with a notable advantage in handling time series field data. This study introduces new ideas and technical approaches for rapid tidal field prediction, overcoming the limitations of traditional methods and providing robust support for coastal disaster prevention, resource management, and environmental protection.