Significant wave height forecasts integrating ensemble empirical mode decomposition with sequence-to-sequence model

Lina Wang; Yu Cao; Xilin Deng; Huitao Liu; Changming Dong

doi:10.1007/s13131-023-2246-y

Volume 42 Issue 10

Oct. 2023

Turn off MathJax

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2023 > 42(10): 54-66

Lina Wang, Yu Cao, Xilin Deng, Huitao Liu, Changming Dong. Significant wave height forecasts integrating ensemble empirical mode decomposition with sequence-to-sequence model[J]. Acta Oceanologica Sinica, 2023, 42(10): 54-66. doi: 10.1007/s13131-023-2246-y

Citation:

Lina Wang, Yu Cao, Xilin Deng, Huitao Liu, Changming Dong. Significant wave height forecasts integrating ensemble empirical mode decomposition with sequence-to-sequence model[J]. Acta Oceanologica Sinica, 2023, 42(10): 54-66. doi: 10.1007/s13131-023-2246-y

Citation:

Lina Wang, Yu Cao, Xilin Deng, Huitao Liu, Changming Dong. Significant wave height forecasts integrating ensemble empirical mode decomposition with sequence-to-sequence model[J]. Acta Oceanologica Sinica, 2023, 42(10): 54-66. doi: 10.1007/s13131-023-2246-y

PDF( 2573 KB)

Significant wave height forecasts integrating ensemble empirical mode decomposition with sequence-to-sequence model

doi: 10.1007/s13131-023-2246-y

Lina Wang^{1, 2},
Yu Cao¹,
Xilin Deng¹,
Huitao Liu¹,
Changming Dong^{2, 3
,
,}

1.
School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science & Technology, Nanjing 210044, China
2.
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
3.
School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China

Funds: 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.

More Information

Corresponding author: E-mail: cmdong@nuist.edu.cn；leader author, E-mail: wangln@nuist.edu.cn
Received Date: 2023-06-10
Accepted Date: 2023-08-15

Available Online: 2023-10-12

Publish Date: 2023-10-01

Abstract

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.
- significant wave height,
- wave forecasting,
- ensemble empirical mode decomposition (EEMD),
- Seq-to-Seq,
- long short-term memory

FullText(HTML)

References(29)

References

Ardhuin F, Stopa J E, Chapron B, et al. 2019. Observing sea states. Frontiers in Marine Science, 6: 124. doi: 10.3389/fmars.2019.00124

Bahdanau D, Cho K, Bengio Y. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv: 1409.0473

Bokde N, Feijóo A, Al-Ansari N, et al. 2020. The hybridization of ensemble empirical mode decomposition with forecasting models: application of short-term wind speed and power modeling. Energies, 13(7): 1666. doi: 10.3390/en13071666

Caires S, Sterl A. 2005. 100-year return value estimates for ocean wind speed and significant wave height from the ERA-40 data. Journal of Climate, 18(7): 1032–1048. doi: 10.1175/JCLI-3312.1

Deo M C, Naidu C S. 1998. Real time wave forecasting using neural networks. Ocean Engineering, 26(3): 191–203. doi: 10.1016/S0029-8018(97)10025-7

Duan W Y, Han Y, Huang L M, et al. 2016. A hybrid EMD-SVR model for the short-term prediction of significant wave height. Ocean Engineering, 124: 54–73. doi: 10.1016/j.oceaneng.2016.05.049

Etemad-Shahidi A, Mahjoobi J. 2009. Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Engineering, 36(15−16): 1175–1181. doi: 10.1016/j.oceaneng.2009.08.008

Fan Shuntao, Xiao Nianhao, Dong Sheng. 2020. A novel model to predict significant wave height based on long short-term memory network. Ocean Engineering, 205: 107298. doi: 10.1016/j.oceaneng.2020.107298

Gong Gangjun, An Xiaonan, Mahato N K, et al. 2019. Research on short-term load prediction based on Seq2seq model. Energies, 12(16): 3199. doi: 10.3390/en12163199

Graves A. 2012. Supervised Sequence Labelling with Recurrent Neural Networks. Heidelberg: Springer Berlin, 37–45

Huang N E, Shen Zheng, Long S R, et al. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971): 903–995

Karatzoglou A, Jablonski A, Beigl M. 2018. A Seq2Seq learning approach for modeling semantic trajectories and predicting the next location. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Washington, Seattle: ACM

Keneshloo Y, Shi Tian, Ramakrishnan N, et al. 2020. Deep reinforcement learning for sequence-to-sequence models. IEEE Transactions on Neural Networks and Learning Systems, 31(7): 2469–2489

Mahjoobi J, Mosabbeb E A. 2009. Prediction of significant wave height using regressive support vector machines. Ocean Engineering, 36(5): 339–347. doi: 10.1016/j.oceaneng.2009.01.001

Nikoo M R, Kerachian R, Alizadeh M R. 2018. A fuzzy KNN-based model for significant wave height prediction in large lakes. Oceanologia, 60(2): 153–168. doi: 10.1016/j.oceano.2017.09.003

Oh J, Suh K D. 2018. Real-time forecasting of wave heights using EOF–wavelet–neural network hybrid model. Ocean Engineering, 150: 48–59. doi: 10.1016/j.oceaneng.2017.12.044

Pirhooshyaran M, Snyder L V. 2020. Forecasting, hindcasting and feature selection of ocean waves via recurrent and sequence-to-sequence networks. Ocean Engineering, 207: 107424. doi: 10.1016/j.oceaneng.2020.107424

Raj N, Brown J. 2021. An EEMD-BiLSTM algorithm integrated with Boruta random forest optimiser for significant wave height forecasting along coastal areas of Queensland, Australia. Remote Sensing, 13(8): 1456. doi: 10.3390/rs13081456

Sutskever I, Vinyals O, Le Q V. 2014. Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge: MIT Press

Vanem E. 2016. Joint statistical models for significant wave height and wave period in a changing climate. Marine Structures, 49: 180–205. doi: 10.1016/j.marstruc.2016.06.001

Vaswani A, Shazeer N, Parmar N, et al. 2017. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc

Wang Lina, Deng Xilin, Ge Peng, et al. 2022. CNN-BiLSTM-attention model in forecasting wave height over South-East China Seas. Computers, Materials & Continua, 73(1): 2151–2168

Wu Zhaohua, Huang N E. 2009. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1): 1–41. doi: 10.1142/S1793536909000047

Yang Shaobo, Deng Zegui, Li Xingfei, et al. 2021. A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast. Renewable Energy, 173: 531–543. doi: 10.1016/j.renene.2021.04.010

Yang Yu, Wang Jun. 2021. Forecasting wavelet neural hybrid network with financial ensemble empirical mode decomposition and MCID evaluation. Expert Systems with Applications, 166: 114097. doi: 10.1016/j.eswa.2020.114097

Ye Lin, Dai Binhua, Pei Ming, et al. 2022. Combined approach for short-term wind power forecasting based on wave division and Seq2Seq model using deep learning. IEEE Transactions on Industry Applications, 58(2): 2586–2596. doi: 10.1109/TIA.2022.3146224

Zhang Yu, Li Yanting, Zhang Guangyao. 2020. Short-term wind power forecasting approach based on Seq2Seq model using NWP data. Energy, 213: 118371. doi: 10.1016/j.energy.2020.118371

Zhou Shuyi, Bethel B J, Sun Wenjin, et al. 2021. Improving significant wave height forecasts using a joint empirical mode decomposition–long short-term memory network. Journal of Marine Science and Engineering, 9(7): 744. doi: 10.3390/jmse9070744

Zhou Shuyi, Xie Wenhong, Lu Yuxiang, et al. 2021. ConvLSTM-based wave forecasts in the South and East China Seas. Frontiers in Marine Science, 8: 680079. doi: 10.3389/fmars.2021.680079

Relative Articles

Supplements(0)

Cited By

Proportional views