Initial Machine Learning Approach of Tidal Current Prediction: The Power of Combined Gaussian Process Regression and One-Dimensional Least Squares Harmonic Method 

Tide monitoring in Pramuka Island (Image by PT Bhumi Warih Geohydromatics)
Figure 1 Conceptualization of sequence of computation for tidal current prediction entailing two essential parts: (i) decomposition of tidal constituents from the time-series elevation of water level according to the LSHM and (ii) applying GPR for data training, i.e. using the decomposed tidal constituents from the earlier phase as predictor against the known tidal current signal, including hyperparameter tuning, from which the GPR model trained for a given site is used to predict tidal current vector involving known tidal signals at the desired period. (Poerbandono et al., 2022)
Figure 2 Velocity of tidal current prediction using 1D-LSHM and control tidal current data (i.e., actual) in easting (top) and northing (below) direction selected from one week of calculation. (Poerbandono et al., 2022)
Figure 3 Velocity of tidal current prediction using GPR and control tidal current data (i.e., actual) in easting (top) and northing (below) direction selected from one week of calculation. (Poerbandono et al., 2022)
Note: u = east current component, v = north current component, RMSE = root mean square error in mm/s, R2 = coefficient of determination, PPRE = percentage of the percentage relative error above 50% in %, N/A = not available due to incapability of LSHM to operate the calculation.