Reconstruction of Tide Gauge Time Series in the Gulf of Guinea Using LSTM Neural Networks with Application to an External Reference Station — Oak Academic Publishing
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Reconstruction of Tide Gauge Time Series in the Gulf of Guinea Using LSTM Neural Networks with Application to an External Reference Station
Advanced School of Mines Processing and Energy Resources of the University of Bertoua, Bertoua, Cameroon
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Association for Research on Ocean Continent Atmosphere, Douala, Cameroon
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Association for Research on Ocean Continent Atmosphere, Douala, Cameroon
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Laboratory of Energy, Materials, Modelling and Methods (LE3M) of the National Higher Polytechnic School of Douala, University of Douala, Douala, Cameroon
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Advanced School of Mines Processing and Energy Resources of the University of Bertoua, Bertoua, Cameroon
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Laboratory of Energy, Materials, Modelling and Methods (LE3M) of the National Higher Polytechnic School of Douala, University of Douala, Douala, Cameroon
,
Laboratory of Energy, Materials, Modelling and Methods (LE3M) of the National Higher Polytechnic School of Douala, University of Douala, Douala, Cameroon
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Technology and Innovation Support Center (TISC) of Advanced School of Mines Processing and Energy Resources of Batouri, Batouri, Cameroon
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Technology and Applied Sciences Laboratory of the University Institute of Technology, University of Douala, Douala, Cameroon
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Department of Environmental Engineering, National Advanced School of Publics Works, Yaoundé, Cameroon
1 Advanced School of Mines Processing and Energy Resources of the University of Bertoua, Bertoua, Cameroon
2 Association for Research on Ocean Continent Atmosphere, Douala, Cameroon
3 Association for Research on Ocean Continent Atmosphere, Douala, Cameroon
4 Laboratory of Energy, Materials, Modelling and Methods (LE3M) of the National Higher Polytechnic School of Douala, University of Douala, Douala, Cameroon
5 Advanced School of Mines Processing and Energy Resources of the University of Bertoua, Bertoua, Cameroon
6 Laboratory of Energy, Materials, Modelling and Methods (LE3M) of the National Higher Polytechnic School of Douala, University of Douala, Douala, Cameroon
7 Laboratory of Energy, Materials, Modelling and Methods (LE3M) of the National Higher Polytechnic School of Douala, University of Douala, Douala, Cameroon
8 Technology and Innovation Support Center (TISC) of Advanced School of Mines Processing and Energy Resources of Batouri, Batouri, Cameroon
9 Technology and Applied Sciences Laboratory of the University Institute of Technology, University of Douala, Douala, Cameroon
10 Department of Environmental Engineering, National Advanced School of Publics Works, Yaoundé, Cameroon
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.
KeywordsReconstruction of Time SeriesTide DataLSTMMissing DataImputationGulf of Guinea
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