Abstract
Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.
Highlights
Wave parameters, especially significant wave height (SWH) and mean wave period (MWP), affect designing marine facilities, such as floating key equipment operations and ship-mooring installation, as well as important environmental factors in marine engineering design
In the Conv-gated recurrent unit (GRU) model, the input data are the form of matrix, while the extracted data exist in terrestrial points, i.e., NANs, which are replaced by 0 considering the physical knowledge and experimental needs
The input variables under different dimensions are normalized to reach the same order of magnitude, subsequently, the output contents are denormalized to return to the previous order of magnitude, where the formulas are shown as Equations (6) and (7), respectively
Summary
Especially significant wave height (SWH) and mean wave period (MWP), affect designing marine facilities, such as floating key equipment operations and ship-mooring installation, as well as important environmental factors in marine engineering design. Data-driven methods, such as statistical and machine learning methods, have focused more on SWH and less attention on MWP They typically apply correlations in the time series to make forecasts without numerical solutions. Machine learning methods seem to be a good modeling tool for stochastic processes, and they are widely applied to the forecasting of non-stationary and nonlinear time series. If they are combined with statistical techniques, they can be faster and, in some cases, more accurate methods.
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