ConvGRU-RMWP: A Regional Multi-Step Model for Wave Height Prediction

Abstract

Accurate large-scale regional wave height prediction is important for the safety of ocean sailing. A regional multi-step wave height prediction model (ConvGRU-RMWP) based on ConvGRU is designed for the two problems of difficult spatial feature resolution and low accuracy of multi-step prediction in ocean navigation wave height prediction. For multi-step prediction, a multi-input multi-output prediction strategy is used, and wave direction and wave period are used as exogenous variables, which are combined with historical wave height data to expand the sample space. For spatial features, a convolutional gated recurrent neural network with an Encoder-Forecaster structure is used to extract and resolve multi-level spatial information. In contrast to time series forecasting methods that consider only backward and forward dependencies in the time dimension and a single assessment of the properties of the predictor variables themselves, this paper additionally considers spatial correlations and implied correlations among the meteorological variables. This model uses the wave height information of the past 24 h to predict the wave height information for the next 12 h. The prediction results in both space and time show that the model can effectively extract spatial and temporal correlations and obtain good multi-step wave height prediction results. The proposed method has a lower prediction error than the other five prediction methods and verifies the applicability of this model for three selected sea areas along the global crude oil transportation route, all of which have a lower prediction error.