Nonstationary significant wave height forecasting with a hybrid VMD-CNN model

Abstract

Significant wave height information is used to measure the intensity of storms and is an important factor in forecasting potential damage in coastal communities, to marine vessels, and to other infrastructure. Under strong nonlinear and nonstationarity conditions, most traditional time series prediction models do not perform well in predicting significant wave heights at a certain location in the sea. This paper proposes a hybrid variational mode decomposition (VMD) and one-dimensional convolutional neural network (1D-CNN) model (VMD-CNN) for nonstationary wave forecasting. The performance of the autoregressive integrated moving average (ARIMA), autoregressive moving average (ARMA), 1D-CNN, and VMD-CNN is studied and verified. In terms of single-step forecasting, the performance of ARMA, ARIMA, 1D-CNN, and VMD-CNN are compared. Moreover, for multistep forecasting, the performance of 1D-CNN and VMD-CNN are compared. Significant wave height data was measured by the WAVERIDER DWR-MKIII wave buoy. The first 90% of the data is used for training, and the rest is used for comparison. The mean square error (MSE), scatter index (SI), coefficient of determination (R2), and time history comparison are used to evaluate the performance of the models. The three metrics indicate that the VMD-CNN model is an effective method for multistep and single-step predictions of nonlinear and nonstationary waves.