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Liu, H., Mi, X. and Li, Y. (2018) Wind Speed Forecasting Method Based on Deep Learning Strategy Using Empirical Wavelet Transform, Long Short Term Memory Neural Network and Elman Neural Network. Energy Conversion and Management, 156, 498-514.
https://doi.org/10.1016/j.enconman.2017.11.053

has been cited by the following article:

• TITLE: Reconstruction of Tide Gauge Time Series in the Gulf of Guinea Using LSTM Neural Networks with Application to an External Reference Station

  AUTHORS: Yves Nzetchouang Mimbeu, Raphaël Onguene, Sakaros Bogning, Alain Tokam Kamga, Olivier Ulrich Igor Owono Amougou, Séverin Nguiya, Ruben Mouangue

  KEYWORDS: Reconstruction of Time Series, Tide Data, LSTM, Missing Data, Imputation, Gulf of Guinea

  JOURNAL NAME: International Journal of Geosciences, Vol.17 No.6, June 23, 2026

  ABSTRACT: Long tide gauge time series are essential for coastal monitoring, port management, and sea level studies, but are often affected by data gaps due to instrumental and operational failures. These gaps hinder reliable analysis and long-term environmental assessment. This study proposes a Long Short-Term Memory (LSTM)-based framework for reconstructing missing data in tide gauge records from the Gulf of Guinea. Three data structuring strategies are designed to address gaps of varying lengths. The proposed model achieves strong performance, with RMSE ≈ 0.05 m, MAPE 2 ≈ 0.96. The reconstructed series are validated using harmonic analysis, demonstrating accurate preservation of tidal dynamics. Additional evaluation through tidal constituent analysis confirms the reliability of the reconstructed data. Results further indicate that the Gulf of Guinea is characterized by an asymmetrical semi-diurnal tidal regime, consistent with existing literature.