Articles | Volume 19, issue 6
https://doi.org/10.5194/os-19-1561-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-19-1561-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Nov 2023
Research article |
| 09 Nov 2023
| 09 Nov 2023
Short-term prediction of the significant wave height and average wave period based on the variational mode decomposition–temporal convolutional network–long short-term memory (VMD–TCN–LSTM) algorithm
Qiyan Ji
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Lei Han
CORRESPONDING AUTHOR
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Lifang Jiang
South China Sea Forecast and Disaster Reduction Center, Ministry of Natural Resources, Guangzhou 510000, China
Yuting Zhang
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Minghong Xie
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Yu Liu
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
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Cited articles
Bai, S., Kolter, J. Z., and Koltun, V.: An Empirical Evaluation of Generic Convolutional and Recurrent Networks, arXiv, abs/1803.01271, https://doi.org/10.48550/arXiv.1803.01271, 2018.
Bento, P. M. R., Pombo, J. A. N., Mendes, R. P. G., Calado, M. R. A., and Mariano, S. J. P. S.: Ocean wave energy forecasting using optimised deep learning neural networks, Ocean Eng., 219, 108372, https://doi.org/10.1016/j.oceaneng.2020.108372, 2021.
Bisoi, R., Dash, P. K., and Parida, A. K.: Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis, Appl. Soft Comput., 74, 652–678, https://doi.org/10.1016/j.asoc.2018.11.008, 2019.
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for coastal regions: 1. Model description and validation, J. Geophys. Res.-Ocean., 104, 7649–7666, https://doi.org/10.1029/98jc02622, 1999.
Bretschneider, C. L.: Hurricane design – Wave practices, J. Waterways Harb. Div., 124, 39–62, 1957.
Brochu, E., Cora, V. M., and Freitas, N. D.: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning, arXiv, abs/1012.2599, https://doi.org/10.48550/arXiv.1012.2599, 2010.
Chen, X., Ding, K., Zhang, J., Han, W., Liu, Y., Yang, Z., and Weng, S.: Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM, Energy, 248, 123574, https://doi.org/10.1016/j.energy.2022.123574, 2022.
Cornejo-Bueno, L., Garrido-Merchán, E. C., Hernández-Lobato, D., and Salcedo-Sanz, S.: Bayesian optimization of a hybrid system for robust ocean wave features prediction, Neurocomputing, 275, 818–828, https://doi.org/10.1016/j.neucom.2017.09.025, 2018.
Cuadra, L., Salcedo-Sanz, S., Nieto-Borge, J. C., Alexandre, E., and Rodríguez, G.: Computational intelligence in wave energy: Comprehensive review and case study, Renew. Sust. Energ. Rev., 58, 1223–1246, https://doi.org/10.1016/j.rser.2015.12.253, 2016.
De Assis Tavares, L. F., Shadman, M., De Freitas Assad, L. P., Silva, C., Landau, L., and Estefen, S. F.: Assessment of the offshore wind technical potential for the Brazilian Southeast and South regions, Energy, 196, 117097, https://doi.org/10.1016/j.energy.2020.117097, 2020.
Deo, M. C. and Naidu, C. S.: Real time wave forecasting using neural networks, Ocean Eng., 26, 191–203, https://doi.org/10.1016/S0029-8018(97)10025-7, 1998.
Dragomiretskiy, K. and Zosso, D.: Variational Mode Decomposition, IEEE Trans. Signal Process., 62, 531–544, https://doi.org/10.1109/tsp.2013.2288675, 2014.
Duan, J., Wang, P., Ma, W., Fang, S., and Hou, Z.: A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting, Int. J. Elec. Power, 134, 107452, https://doi.org/10.1016/j.ijepes.2021.107452, 2022.
Fan, S., Xiao, N., and Dong, S.: A novel model to predict significant wave height based on long short-term memory network, Ocean Eng., 205, 107298, https://doi.org/10.1016/j.oceaneng.2020.107298, 2020.
Frazier, P. I.: A tutorial on bayesian optimization, arXiv, https://doi.org/10.48550/arXiv.1807.02811, 2018.
Fu, W., Fang, P., Wang, K., Li, Z., Xiong, D., and Zhang, K.: Multi-step ahead short-term wind speed forecasting approach coupling variational mode decomposition, improved beetle antennae search algorithm-based synchronous optimization and Volterra series model, Renew. Energ., 179, 1122–1139, https://doi.org/10.1016/j.renene.2021.07.119, 2021.
Gao, S., Huang, J., Li, Y., Liu, G., Bi, F., and Bai, Z.: A forecasting model for wave heights based on a long short-term memory neural network, Acta Oceanol. Sin., 40, 62–69, https://doi.org/10.1007/s13131-020-1680-3, 2021.
Gers, F. A., Schmidhuber, J., and Cummins, F.: Learning to Forget Continual Prediction with LSTM, Neural Comput., 12, 2451–2471, https://doi.org/10.1162/089976600300015015, 2000.
Hao, W., Sun, X., Wang, C., Chen, H., and Huang, L.: A hybrid EMD-LSTM model for non-stationary wave prediction in offshore China, Ocean Eng., 246, 110566, https://doi.org/10.1016/j.oceaneng.2022.110566, 2022.
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Comput., 9, 1735–1780 https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Hu, H., van der Westhuysen, A. J., Chu, P., and Fujisaki-Manome, A.: Predicting Lake Erie wave heights and periods using XGBoost and LSTM, Ocean Model., 164, 101832, https://doi.org/10.1016/j.ocemod.2021.101832, 2021.
Hua, L., Zhang, C., Peng, T., Ji, C., and Shahzad Nazir, M.: Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction, Energ. Convers. Manage., 252, 115102, https://doi.org/10.1016/j.enconman.2021.115102, 2022.
Huang, J., Chen, Q., and Yu, C.: A New Feature Based Deep Attention Sales Forecasting Model for Enterprise Sustainable Development, Sustainability, 14, 12224, https://doi.org/10.3390/su141912224, 2022.
Huang, Y. and Deng, Y.: A new crude oil price forecasting model based on variational mode decomposition, Knowl-Based Syst., 213, 106669, https://doi.org/10.1016/j.knosys.2020.106669, 2021.
Jain, P. and Deo, M. C.: Neural networks in ocean engineering, Ships Offshore Struc., 1, 25–35, https://doi.org/10.1533/saos.2004.0005, 2006.
Jain, P., Deo, M. C., Latha, G., and Rajendran, V.: Real time wave forecasting using wind time history and numerical model, Ocean Model., 36, 26–39, https://doi.org/10.1016/j.ocemod.2010.07.006, 2011.
Jamei, M., Ali, M., Karbasi, M., Xiang, Y., Ahmadianfar, I., and Yaseen, Z. M.: Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach, Appl. Energ., 326, 119925, https://doi.org/10.1016/j.apenergy.2022.119925, 2022.
Kamranzad, B., Etemad-Shahidi, A., and Kazeminezhad, M. H.: Wave height forecasting in Dayyer, the Persian Gulf, Ocean Eng., 38, 248–255, https://doi.org/10.1016/j.oceaneng.2010.10.004, 2011.
Karbasi, M., Jamei, M., Ali, M., Abdulla, S., Chu, X., and Yaseen, Z. M.: Developing a novel hybrid Auto Encoder Decoder Bidirectional Gated Recurrent Unit model enhanced with empirical wavelet transform and Boruta-Catboost to forecast significant wave height, J. Clean. Prod., 379, 134820, https://doi.org/10.1016/j.jclepro.2022.134820, 2022.
Kok, C., Jahmunah, V., Oh, S. L., Zhou, X., Gururajan, R., Tao, X., Cheong, K. H., Gururajan, R., Molinari, F., and Acharya, U. R.: Automated prediction of sepsis using temporal convolutional network, Comput. Biol. Med., 127, 103957, https://doi.org/10.1016/j.compbiomed.2020.103957, 2020.
Li, B., Zhang, J., He, Y., and Wang, Y.: Short-Term Load-Forecasting Method Based on Wavelet Decomposition With Second-Order Gray Neural Network Model Combined With ADF Test, IEEE Access, 5, 16324–16331, https://doi.org/10.1109/ACCESS.2017.2738029, 2017.
Li, H., Liu, T., Wu, X., and Chen, Q.: An optimized VMD method and its applications in bearing fault diagnosis, Measurement, 166, 108185, https://doi.org/10.1016/j.measurement.2020.108185, 2020.
Li, W., Wei, Y., An, D., Jiao, Y., and Wei, Q.: LSTM-TCN: dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network, Environ. Sci. Pollut. Res. Int., 29, 39545–39556, https://doi.org/10.1007/s11356-022-18914-8, 2022.
Li, X., Cao, J., Guo, J., Liu, C., Wang, W., Jia, Z., and Su, T.: Multi-step forecasting of ocean wave height using gate recurrent unit networks with multivariate time series, Ocean Eng., 248, 110689, https://doi.org/10.1016/j.oceaneng.2022.110689, 2022.
Liu, Y., Yang, C., Huang, K., and Gui, W.: Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network, Knowl-Based Syst., 188, 105006, https://doi.org/10.1016/j.knosys.2019.105006, 2020.
Luo, Y., Shi, H., Zhang, Z., Zhang, C., Zhou, W., Pan, G., and Wang, W.: Wave field predictions using a multi-layer perceptron and decision tree model based on physical principles: A case study at the Pearl River Estuary, Ocean Eng., 277, 114246, https://doi.org/10.1016/j.oceaneng.2023.114246, 2023.
Mafi, S. and Amirinia, G.: Forecasting hurricane wave height in Gulf of Mexico using soft computing methods, Ocean Eng., 146, 352–362, https://doi.org/10.1016/j.oceaneng.2017.10.003, 2017.
Makarynskyy, O.: Improving wave predictions with artificial neural networks, Ocean Eng., 31, 709–724, https://doi.org/10.1016/j.oceaneng.2003.05.003, 2004.
Malekmohamadi, I., Ghiassi, R., and Yazdanpanah, M. J.: Wave hindcasting by coupling numerical model and artificial neural networks, Ocean Eng., 35, 417–425, https://doi.org/10.1016/j.oceaneng.2007.09.003, 2008.
Neshat, M., Nezhad, M. M., Sergiienko, N. Y., Mirjalili, S., Piras, G., and Garcia, D. A.: Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser, Energy, 256, 124623, https://doi.org/10.1016/j.energy.2022.124623, 2022.
Neumann, G. and Pierson, W. J.: A detailed comparison of theoretical wave spectra and wave forecasting methods, Deutsche Hydrographische Zeitschrift, 10, 134–146, https://doi.org/10.1007/BF02020059, 1957.
Ni, C. and Ma, X.: An integrated long-short term memory algorithm for predicting polar westerlies wave height, Ocean Eng., 215, 107715, https://doi.org/10.1016/j.oceaneng.2020.107715, 2020.
Nitsure, S. P., Londhe, S. N., and Khare, K. C.: Wave forecasts using wind information and genetic programming, Ocean Eng., 54, 61–69, https://doi.org/10.1016/j.oceaneng.2012.07.017, 2012.
Niu, D., Ji, Z., Li, W., Xu, X., and Liu, D.: Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization, Energy, 234, 121145, https://doi.org/10.1016/j.energy.2021.121145, 2021.
Pushpam P., M. M. and Enigo V.S., F.: Forecasting Significant Wave Height using RNN-LSTM Models, In Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 13–15 May 2020, 1141–1146, https://doi.org/10.1109/ICICCS48265.2020.9121040, IEEE, 2020.
Rao, A. D., Sinha, M., and Basu, S.: Bay of Bengal wave forecast based on genetic algorithm: A comparison of univariate and multivariate approaches, Appl. Math. Model., 37, 4232–4244, https://doi.org/10.1016/j.apm.2012.09.001, 2013.
Rasmussen, C. E.: Gaussian Processes in Machine Learning, in: Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, 2–14 February 2003, Tübingen, Germany, 4–16 August 2003, Revised Lectures, edited by: Bousquet, O., von Luxburg, U., and Rätsch, G., Springer Berlin Heidelberg, Berlin, Heidelberg, 63–71, https://doi.org/10.1007/978-3-540-28650-9_4, 2004.
Rogers, W. E., Hwang, P. A., and Wang, D. W.: Investigation of wave growth and decay in the SWAN model: Three Regional-Scale applications, J. Phys. Oceanogr., 33, 366–389, https://doi.org/10.1175/1520-0485(2003)033<0366:Iowgad>2.0.Co;2, 2003.
Sverdrup, H. and Munk, W.: Wind, sea and swell. Theory of relations for forecasting, Navy Hydrographic office, Washington, DC, 601, 1947.
Tolman, H. L.: User manual and system documentation of WAVEWATCH III version 3.14, Analysis, 166, 2009.
Wamdi, G.: The WAM model – a third generation ocean wave prediction model, J. Phys. Oceanogr., 18, 1775–1810, https://doi.org/10.1175/1520-0485(1988)0182.0.CO;2, 1988.
Wang, W., Tang, R., Li, C., Liu, P., and Luo, L.: A BP neural network model optimized by Mind Evolutionary Algorithm for predicting the ocean wave heights, Ocean Eng., 162, 98–107, https://doi.org/10.1016/j.oceaneng.2018.04.039, 2018.
Wu, Q. and Lin, H.: Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network, Sustain. Cities Soc., 50, 101657, https://doi.org/10.1016/j.scs.2019.101657, 2019.
Xu, Y. H., Hu, C. H., Wu, Q., Li, Z. C., Jian, S. Q., and Chen, Y. Q.: Application of temporal convolutional network for flood forecasting, Hydrol. Res., 52, 1455–1468, https://doi.org/10.2166/nh.2021.021, 2021.
Yan, J., Mu, L., Wang, L., Ranjan, R., and Zomaya, A. Y.: Temporal Convolutional Networks for the Advance Prediction of ENSO, Sci. Rep., 10, 8055, https://doi.org/10.1038/s41598-020-65070-5, 2020.
Yu, Y., Si, X., Hu, C., and Zhang, J.: A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures, Neural Comput., 31, 1235–1270, https://doi.org/10.1162/neco_a_01199, 2019.
Zhang, Q., Hu, W., Liu, Z., and Tan, J.: TBM performance prediction with Bayesian optimization and automated machine learning, Tunn. Undergr. Space Technol., 103, 103493, https://doi.org/10.1016/j.tust.2020.103493, 2020a.
Zhang, Y., Pan, G., Chen, B., Han, J., Zhao, Y., and Zhang, C.: Short-term wind speed prediction model based on GA-ANN improved by VMD, Renew. Energ., 156, 1373–1388, https://doi.org/10.1016/j.renene.2019.12.047, 2020b.
Zhang, Z., Wang, X., and Jung, C.: DCSR: Dilated Convolutions for Single Image Super-Resolution, IEEE Trans. Image Process., 28, 1625–1635, https://doi.org/10.1109/TIP.2018.2877483, 2019.
Zhao, W., Gao, Y., Ji, T., Wan, X., Ye, F., and Bai, G.: Deep Temporal Convolutional Networks for Short-Term Traffic Flow Forecasting, IEEE Access, 7, 114496–114507, https://doi.org/10.1109/access.2019.2935504, 2019.
Zhou, S., Bethel, B. J., Sun, W., Zhao, Y., Xie, W., and Dong, C.: Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network, J. Mar. Sci. Eng., 9, 744, https://doi.org/10.3390/jmse9070744, 2021.
Zhou, T., Wu, W., Peng, L., Zhang, M., Li, Z., Xiong, Y., and Bai, Y.: Evaluation of urban bus service reliability on variable time horizons using a hybrid deep learning method, Reliab. Eng. Syst. Safe., 217, 108090, https://doi.org/10.1016/j.ress.2021.108090, 2022.
Zuo, G., Luo, J., Wang, N., Lian, Y., and He, X.: Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting, J. Hydrol., 585, 124776, https://doi.org/10.1016/j.jhydrol.2020.124776, 2020.
Short summary
Accurate wave forecasts are essential to marine engineering safety. The research designs a model with combined signal decomposition and multiple neural network algorithms to predict wave parameters. The hybrid wave prediction model has good robustness and generalization ability. The contribution of the various algorithms to the model prediction skill was analyzed by the ablation experiments. This work provides a neoteric view of marine element forecasting based on artificial intelligence.
Accurate wave forecasts are essential to marine engineering safety. The research designs a model...
