Tidal Level Prediction Model Based on VMD-LSTM Neural Network

The fluctuation of the tide is closely related to the production and life of people in coastal areas, and the change in the tide level will have a significant impact on the safety of infrastructure, ship travel, ecological environment, and other issues. Therefore, it is of great significance to analyze, study, and forecast the change in tide level. Aiming at the complex characteristics of nonlinearity, time-varying dynamics, and uncertainty generated by celestial bodies’ movements and influenced by geographical as well as hydrometeorological factors, this paper proposes a combined model based on variational mode decomposition (VMD) and long short-term memory neural networks (LSTM). A tidal level prediction procedure is proposed by combining the harmonic analysis method with a neural network and takes the point tide data of Luchao Port from 2021 to 2022 as the applied data. First, the VMD algorithm decomposes the tidal level data into model components. Then, the LSTM model is used to predict each component. Finally, the predicted value of each component is superposed to obtain the final prediction result. Standard evaluation indexes were used to analyze the performance of the proposed model. The model’s RMSE, MAE, MAPE, and R2 were 0.0385, 0.0267, 5.8327, and 99.91%, respectively, superior to other compared models (BP, SVM, and LSTM). This study can provide a reference for tidal level prediction. These results show that the VMD-LSTM model is an effective and reliable tidal level prediction tool with considerable potential in offshore engineering and maritime management.