A combined Gaussian process regression and one-dimensional least squares harmonic method for tidal current prediction
Predictions of tidal currents are required for various activities in the maritime industry, including marine power exploitation, operational forecasting, and coastal engineering. In addition to the conventional least squares harmonic method (LSHM) and numerical simulation, a machine learning algorit...
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| Published in: | Estuarine, coastal and shelf science Vol. 275; p. 107964 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
30-09-2022
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Predictions of tidal currents are required for various activities in the maritime industry, including marine power exploitation, operational forecasting, and coastal engineering. In addition to the conventional least squares harmonic method (LSHM) and numerical simulation, a machine learning algorithm has recently been developed and applied for predicting tidal currents in time series. Gaussian Process Regression (GPR) is one of the most important machine learning algorithms due to its ability to process time-series data. In this paper, we propose a combined one-dimensional LSHM and GPR method for predicting depth-averaged current velocity vectors according to time-series changes of water surface elevation due to tide. LSHM firstly decomposes water surface elevation time series into a number of possible constituents (i.e., amplitudes, phase-lags) according to known tidal frequencies. For each of the contributing constituents, GPR is then applied to produce the corresponding current generator using known tidal current as training data. The training data are generated by numerical simulations representing different coastal settings. The predicted tidal current is calculated according to the previously trained GPR model. As a comparison, we also generated tidal current predictions using a conventional two-dimensional LSHM. It is found that the tidal currents predicted using the sequence of computations proposed in this paper show comparable results with slightly better accuracy than conventional two-dimensional LSHM. The results from this study could provide an alternative method of tidal current prediction, minimising the need for the laborious re-runs of numerical simulations and lengthy current observations, as well as removing the requirement to carry out a two-dimensional analysis of the tidal current using LSHM.
•Gaussian process regression and 1D least squares harmonic methods are combined.•Predicts depth-averaged tidal current from tidal elevation in time series.•Tested in six locations with various depths and distances from the nearest shoreline.•Performs as good as or better than 2D least square harmonic method.•Results potentially reduce the need for long-term current observation. |
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| ISSN: | 0272-7714 1096-0015 |
| DOI: | 10.1016/j.ecss.2022.107964 |