Research on Non-Stationary Tidal Level Prediction Based on SVMD and BiLSTM

Abnormal tidal levels pose a serious threat to maritime navigation, coastal infrastructure, and human life and property. Therefore, it is crucial to accurately predict tidal levels. However, due to the influence of topography and meteorology, tidal levels exhibit complex and non-stationary characteristics, making high-precision prediction a significant challenge. This study proposes a tidal prediction model, named SVMD-BiLSTM-Residual Decomposition (SBRD), which combines Successive Variational Mode Decomposition (SVMD) and Bidirectional Long Short-Term Memory (BiLSTM) networks. SBRD decomposes non-stationary tidal signals into simpler intrinsic mode functions (IMFs) using SVMD. Each IMF is then independently predicted using a BiLSTM network, and the final prediction is obtained through signal reconstruction. Experimental results show that SBRD accurately predicts tidal levels within a 24 h horizon and maintains robust performance during abnormal tidal events, such as acqua alta. Compared to other models, SBRD achieves the highest prediction accuracy and the lowest error, with a Coefficient of Determination (R2) exceeding 99%, a Mean Absolute Error (MAE) of 1.33 cm or less, and a Root Mean Square Error (RMSE) within 2.13 cm for tidal forecasts within a 24 h horizon. These results demonstrate that SBRD effectively enhances the accuracy of tidal level prediction, contributing to the advancement of marine economic technologies and the prevention and mitigation of marine disasters.