Abstract

Continuous recordings of significant wave height (SWH) are of significant importance to facilitate the development and application of wave energy assessments. However, SWH recording is often interrupted by a variety of factors, including severe weather, apparatus failure, etc. The presence of short-term intermittent and longterm continuous missing values is recognized as a significant challenge for the accurate analysis and energy assessment of wave data. To effectively fill in the short- and long-term missing values simultaneously, a hybrid Kalman smoothing (KS) Long Short-Term Memory (LSTM) model, which is applied to fill in SWH for the first time, is proposed. Initially, the short-term intermittent missing values, with missing ratios of 10%-50%, are filled by using the KS method, achieving a root mean square error (RMSE) as low as 0.082. Based on short-term missing values addressed by KS, the LSTM model is introduced to effectively predict the long-term continuous missing values. The maximum RMSE reduction rate of KS-LSTM is reduced by 49.6%, 59.4%, and 57.8% compared to KSARIMA, LSTM, and ARIMA, respectively, within the short-term intermittent missing ratios of 10%-50%. The maximum reduction in mean square error (MSE) is observed to be 71.4%, 83.6%, and 82.1%. Similarly, the maximum reduction in mean absolute error (MAE) is 74.6%, 61.1%, and 55.5%. The lowest prediction RMSE of long-term missing values by KS-LSTM is only 0.091, which demonstrates that the effectiveness of filling in both short-term and long-term missing values simultaneously by the KS-LSTM.