Abstract
As wave height is an important parameter in marine climate measurement, its accurate prediction is crucial in ocean engineering. It also plays an important role in marine disaster early warning and ship design, etc. However, challenges in the large demand for computing resources and the improvement of accuracy are currently encountered. To resolve the above mentioned problems, sequence-to-sequence deep learning model (Seq-to-Seq) is applied to intelligently explore the internal law between the continuous wave height data output by the model, so as to realize fast and accurate predictions on wave height data. Simultaneously, ensemble empirical mode decomposition (EEMD) is adopted to reduce the non-stationarity of wave height data and solve the problem of modal aliasing caused by empirical mode decomposition (EMD), and then improves the prediction accuracy. A significant wave height forecast method integrating EEMD with the Seq-to-Seq model (EEMD-Seq-to-Seq) is proposed in this paper, and the prediction models under different time spans are established. Compared with the long short-term memory model, the novel method demonstrates increased continuity for long-term prediction and reduces prediction errors. The experiments of wave height prediction on four buoys show that the EEMD-Seq-to-Seq algorithm effectively improves the prediction accuracy in short-term (3-h, 6-h, 12-h and 24-h forecast horizon) and long-term (48-h and 72-h forecast horizon) predictions.
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Foundation item: The Project Supported by Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No.SML2020SP007; the National Natural Science Foundation of China under contract Nos 42192562 and 62072249.
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Wang, L., Cao, Y., Deng, X. et al. Significant wave height forecasts integrating ensemble empirical mode decomposition with sequence-to-sequence model. Acta Oceanol. Sin. 42, 54–66 (2023). https://doi.org/10.1007/s13131-023-2246-y
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DOI: https://doi.org/10.1007/s13131-023-2246-y