Deep Learning for Imputation and Forecasting Tidal Level

Tidal observations influence the transport efficiency of international commercial ports and can be affected by mechanical failures or typhoon-induced storms. These factors cause observational interruptions, which lead to tidal data loss or anomaly. Thus, the applicability of the data is reduced. Existing methods still have certain limitations in accurately predicting the tide level because of the omission of a large amount of data. Therefore, missing value imputation and tide level forecasting of tidal data are crucial topics in tidal observation study. In this study, we propose a deep learning algorithm for missing value imputation and tide level forecasting of tidal data. The test data are obtained from the tidal data of Keelung Port, Taipei Port, Tamsui Port, Taichung Port, Jiangjun Port, Anping Port, Kaohsiung Port, Hualian Port, Suao Port, and Penghu Port, constructed by the Harbor and Marine Technology Center, Taiwan. The average error value for conducting missing value imputation is 0.086 m +/- 5%, and the average error value for tide level forecasting is 0.071 m. The experimental results reveal that the deep neural network has better performance than the traditional statistical methods and other artificial neural networks.

Automated summary

Tidal observations can be influenced by mechanical failures or typhoon-induced storms, leading to data interruptions or anomalies, reducing data applicability. A deep learning algorithm for missing value imputation and tide level forecasting has been proposed, with experimental results showing better performance compared to traditional methods.