Article Details

Abstract

Owing to the influence of environmental factors such as wind, wave and tide, traditional methods of tidal range and analysis are incapable of accurately capturing the complex features of tidal time series data. To solve this problem, this paper proposes a method based on the LSTM neural network to predict the tide level at the Lianyungang station. The LSTM model is constructed by setting different parameters, including the number of LSTM layers, the batch size, the number of hidden layer nodes, initial learning rate, and sequence length. A dataset composed of hourly tidal data from the Lianyungang tide gauge station since January 2022 is used to train the model, and the performance of the model under different network parameter settings is evaluated. Finally, the optimal model parameters are selected to predict the future tidal data of Lianyungang. The analysis of the prediction results demonstrates that the model can perform the prediction task quite well.

Keywords

tidal data prediction; Deep Learning; LSTM model

References

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About the authors

Ling Xinhui (2002-), male, Han ethnicity, from Yangzhou, Jiangsu, is currently pursuing a bachelor's degree with a research focus on electronic information; Shang Yujie (2003-), male, Han ethnicity, from Bengbu, Anhui Province, is currently pursuing a bachelor's degree with a research focus on electronic information.