Correction of nonstationary tidal prediction using deep-learning neural network models in tidal estuaries and rivers

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An operational tidal prediction method based on a combination of nonstationary harmonic analysis and deep-learning neural network models is described. Nonstationary harmonic analysis (NSHA), which was based on classical harmonic analysis (CHA), has been developed for tidal forecasting in tidal rivers and estuaries. However, the prediction accuracy is poor when the discharge grows very large or changes abruptly. Therefore, in this study, we aimed to combine nonstationary harmonic analysis with a deep-learning neural network model to improve tidal forecasts in tidal rivers and estuaries. The long short-term memory (LSTM) neural network model, which works well for processing long-term time series data, was chosen to correct the errors from NSHA. In addition, the traditional feed-forward neural network (FFNN) model was also applied and compared with LSTM to determine and optimize the structure of the neural network. Through experiments, the results showed that a two-layer network with a fully connected layer on top of an LSTM layer that uses discharge in addition to the previous time series as input data exhibited the best performance in predicting the errors of the NS_TIDE model. After correction by the proposed model at three stations on the West River, a branch river of the Pearl River, the root mean square error (RMSE) at 24 h by the NS_TIDE model can be reduced from approximately 0.3 m to less than 0.1 m. More specifically, the results indicated a significant improvement for extremely high-level prediction, which is crucial for water conservancy administrations. Finally, optimization approaches of the neural network to prevent overfitting and improve efficiency are also discussed.