Point and interval prediction for significant wave height based on LSTM-GRU and KDE

Abstract

Accurate wave height prediction is significant for wave power generation, maritime traffic, and disaster prevention and reduction. This paper proposes a novel point and interval prediction framework based on deep learning methods for wave height prediction. First, multidimensional and multisource datasets are preprocessed. Subsequently, a feature selection process is executed to remove superfluous features. Third, a hybrid model that combines long short-term memory (LSTM) and a gated recurrent unit (GRU) is used to predict wave height. This model uses multivariate features that are positively correlated with wave height as input data. Finally, kernel density estimation (KDE) is leveraged to estimate the probability density distribution of the prediction errors generated the wave height prediction interval. The proposed LSTM-GRU-KDE forecasting framework is compared with benchmark models to verify its practicability. The LSTM-GRU model with multiple feature inputs has lower root mean square error and mean absolute error values and a higher coefficient of determination in predicting hourly wave height for all buoy stations compared to those of LSTM with multiple feature inputs and LSTM-GRU with a single feature input. Moreover, by the coverage width-based criterion, KDE (box) stands out as a superior choice among interval forecasting methods, offering a higher degree of reliability when constructing prediction intervals.