Deep Learning Ensemble Approach for Predicting Significant Wave Height Using N-BEATS
Keywords:
N-BEATS, TCN, LSTM, MLP, deep learning, multi-model ensemble, Numerical Weather PredictionsAbstract
Accurate prediction of the physical parameters of the ocean, like wave height, wave period, etc., is of paramount importance when forewarning the coastal community of imminent threats. The forecast of wave heights is presently being generated using state-of-the-art numerical wave models like Mike 21SW, Wave Watch III, Swan, etc. The study leverages a pure deep learning architecture (N-BEATS) to generate more accurate wave height predictions for the multi-model ensemble. For the ensemble process, the observation data collected by the coastal open ocean buoys and the forecast generated by various models for one year have been considered. Performance investigation using Brier Skill Score (BSS) and Taylor diagrams has indicated that the N-BEATS Ensemble forecast has outperformed not only the numerical weather predictions (NWP) but also other neural engines such as temporal convolution networks (TCN), long short-term memory networks (LSTM), and multi-layer perceptron models. The performance of the N-Beats Ensemble Forecast approach during cyclonic events in the Bay of Bengal and the Arabian Sea in 2021 indicated an improved correlation with minimal RMSE
Downloads
References
Balakrishnan Nair, T., Pachalla, S., G, S.K., Sanil Kumar, V.: Performance of the Ocean State Forecast system at Indian National Centre for Ocean Information Services. Technical report (2013)
Sirisha, P., Remya, P.G., Janardhanan, J., Nair, T.M.B.: Seasonal variation of wave power potential in the coastal areas of India. Current Science 122(5), 584–590 (2022)
Vethamony, P., Sudheesh, K., Rupali, S., Babu, M., Jayakumar, S., Saran, A., Basu, S., Kumar, R., Sarkar, A.: Wave modelling for the north indian ocean using msmr analysed winds. International Journal of Remote Sensing 27(18), 3767–3780 (2006)
Komen, G.J., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., Janssen, P.A.E.M.: Dynamics and Modelling of Ocean Waves. Dynamics and Modelling of Ocean Waves (1994)
Oo, Y.H., Zhang, H.: Spatial wave assimilation by integration of artificial neural network and numerical wave model. Ocean Engineering 247, 110752 (2022)
Remya, P.G., Rabi Ranjan, T., Sirisha, P., Harikumar, R., Balakrishnan Nair, T.M.: Indian Ocean wave forecasting system for wind waves: development and its validation. Journal of Operational Oceanography 15(1), 1–16 (2022)
Naseef T, M., Rajkumar, J., Jena, B.K.: Sensitivity analysis of surface wave hindcasts for the Indian Ocean, pp. 1–9. Institute of Electrical and Electronics Engineers (IEEE), ??? (2022)
Harikumar, R., Balakrishnan Nair, T.M., Rao, B.M., Prasad, R., Ramakrishna Phani, P., Nagaraju, C., Ramesh Kumar, M., Jeyakumar, C., Shenoi, S.S.C., Nayak, S.: Ground-zero met-ocean observations and attenuation of wind energy during cyclonic storm Hudhud. Current Science 110(12), 2245–2252 (2016)
Sanil Kumar, V., Mandal, S.: Characteristics of individual surface waves measured by moored buoys in the coastal waters of the eastern Arabian Sea. Ocean Engineering 248 (2022)
Shanas, P.R., Kumar, V.S., George, J., Joseph, D., Singh, J.: Observations of surface wave fields in the Arabian Sea under tropical cyclone Tauktae. Ocean Engineering 242 (2021)
Mahjoobi, J., Mosabbeb, E.A.: Prediction of significant wave height using regressive support vector machines. Ocean Engineering 36(5), 339–347 (2009)
Majumder, S., Balakrishnan Nair, T.M., Sandhya, K.G., Remya, P.G., Sirisha, P.: Modification of a linear regression-based multi-model superensemble technique and its application in forecasting of wave height during extreme weather conditions. Journal of Operational Oceanography 11(1), 1–10 (2018)
Adnan, R.M., Sadeghifar, T., Alizamir, M., Azad, M.T., Makarynskyy, O., Kisi, O., Barati, R., Ahmed, K.O.: Short-term probabilistic prediction of significant wave height using bayesian model averaging: Case study of chabahar port, iran. Ocean Engineering 272, 113887 (2023)
Song, T., Han, R., Meng, F., Wang, J., Wei, W., Peng, S.: A significant wave height prediction method based on deep learning combining the correlation between wind and wind waves. Frontiers in Marine Science 9, 983007 (2022)
Shamshirband, S., Mosavi, A., Rabczuk, T., Nabipour, N., Chau, K.w.: Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines. Engineering Applications of Computational Fluid Mechanics 14(1), 805–817 (2020)
Huang, J., Osorio, C., Sy, L.W.: An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes. Computer Methods and Programs in Biomedicine 177, 141–153 (2019)
Minuzzi, F.C., Farina, L.: A deep learning approach to predict significant wave height using long short-term memory. Ocean Modelling 181, 102151 (2023)
Erdebilli, B., Devrim-I¸ctenba¸s, B.: Ensemble voting regression based˙ on machine learning for predicting medical waste: a case from turkey. Mathematics (2022)
Ren, Y., Zhang, L., Suganthan, P.N.: Ensemble classification and regression-recent developments, applications and future directions. IEEE Computational intelligence magazine (2016)
Hansen, B.E., Racine, J.S.: Jackknife model averaging. Journal of Econometrics (2012)
Garthwaite, P.H., Mubwandarikwa, E.: Selection of weights for weighted model averaging. Australian & New Zealand Journal of Statistics 52(4), 363–382 (2010)
Montero-Manso, P., Athanasopoulos, G., Hyndman, R.J., Talagala, T.S.: Fforma: Feature-based forecast model averaging. International Journal of Forecasting 36(1), 86–92 (2020)
Cawood, P., van Zyl, T.: Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion (2022)
Li, D., Jiang, F., Chen, M., Qian, T.: Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks. Energy 238 (2022)
Gao, R., Li, R., Hu, M., Suganthan, P.N., Yuen, K.F.: Dynamic ensemble deep echo state network for significant wave height forecasting. Applied Energy 329, 120261 (2023)
Dong, X., Yu, Z., Cao, W., Shi, Y., Ma, Q.: A survey on ensemble learning. Frontiers of Computer Science 14, 241–258 (2020)
Sagi, O., Rokach, L.: Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(4), 1249 (2018)
Dietterich, T.G., et al.: Ensemble learning. The handbook of brain theory and neural networks 2(1), 110–125 (2002)
Abdelmigid, T.A., Shehata, A.S., Abdel-Geliel, M., Ahmed, Y.M., Kotb, M.: Improved short-term significant wave height forecasting using ensemble empirical mode decomposition coupled with linear regression. In: AIP Conference Proceedings, vol. 2769 (2023). AIP Publishing
Gao, R., Li, R., Hu, M., Suganthan, P.N., Yuen, K.F.: Significant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning. Engineering Applications of Artificial Intelligence 117, 105535 (2023)
Fu, Y., Ying, F., Huang, L., Liu, Y.: Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and lstm. Renewable Energy 203, 455–472 (2023)
Sareen, K., Panigrahi, B.K., Shikhola, T., Nagdeve, R.: An integrated decomposition algorithm based bidirectional lstm neural network approach for predicting ocean wave height and ocean wave energy. Ocean Engineering 281, 114852 (2023)
Zhou, Z., Zhong, X., Zhang, J., Ren, Z., Wang, K., Sun, Y.: Assess the swell-driven extreme wave conditions in ghana coast through numerical simulation. In: 18th Annual Meeting of the Asia Oceania Geosciences Society: Proceedings of the 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021), pp. 165–167 (2022). World Scientific
Abu Zed, A.A., Kansoh, R.M., Iskander, M.M., Elkholy, M.: Wind and wave climate southeastern of the Mediterranean Sea based on a highresolution SWAN model. Dynamics of Atmospheres and Oceans 99, 101311 (2022)
Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019)
Chatigny, P., Wang, S., Patenaude, J.-M., Oreshkin, B.N.: Neural forecasting at scale (2021)
Stevenson, E., Rodriguez-Fernandez, V., Minisci, E., Camacho, D.: A deep learning approach to solar radio flux forecasting. Acta Astronautica 193, 595–606 (2022)
Hao, W., Sun, X., Wang, C., Chen, H., Huang, L.: A hybrid EMDLSTM model for non-stationary wave prediction in offshore China. Ocean Engineering 246 (2022)
Bai, S., Kolter, J.Z., Koltun, V.: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (2018)
Kajino, M., Tanji, N., Kuramochi, M.: Better prediction of surface ozone by a superensemble method using emission sensitivity runs in Japan. Atmospheric Environment: X 12 (2021)
Lin, Z., Adcock, T.A.A., McAllister, M.L.: Estimating ocean wave directional spreading using wave following buoys: a comparison of experimental buoy and gauge data. Journal of Ocean Engineering and Marine Energy 8(1), 83–97 (2022)
Pakdaman, M., Babaeian, I., Javanshiri, Z., Falamarzi, Y.: European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation. Water Resources Management 36(2), 611–623 (2022)
James, J.P., Panchang, V.: Investigation of Wave Height Distributions and Characteristic Wave Periods in Coastal Environments. Journal of Geophysical Research: Oceans 127(5) (2022)
Oreshkin, B.N., Dudek, G., Pel ka, P., Turkina, E.: N-BEATS neural network for mid-term electricity load forecasting. Applied Energy 293 (2021)
Ventura -Traveset, J., Mathur, A.R., Toran, F., Plag, H.P., Sage, A.: Provision of emergency communication messages through SBAS: The ESA ALIVE concept
Menon K Krishnamurthy, S.S., Saeed, F., Mani Thomas, R., Zhang, Y., Krishnamurthy, K., Menon, S.S.: Scholars’ Mine Scholars’ Mine Multimodal Ensemble Deep Learning to Predict Disruptive Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children Behavior Disorders in Children Multimodal Ensemble Deep Learning to Predict Disruptive Behavior Disorders in Children. Frontiers in Neuroinformatics 15, 742807 (2021)
Singh, N.P., Joshi, A.R., Alam, M.N.: Short-term forecasting in smart electric grid using n-beats. In: 2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T), pp. 1–6 (2022). IEEE
Lenartz, F., Beckers, J.M., Chiggiato, J., Mourre, B., Troupin, C., Vandenbulcke, L., Rixen, M.: Super-ensemble techniques applied to wave forecast: Performance and limitations. Ocean Science 6(2), 595–604 (2010)
Alessandrini, S., Sperati, S., Pinson, P.: The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation. Adv. Sci. Res 8, 2011 (2012)
Hunt, K.M.R., Matthews, G.R., Pappenberger, F., Prudhomme, C.: Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States
Chen, D., Liu, F., Zhang, Z., Lu, X., Li, Z.: Significant wave height prediction based on wavelet graph neural network. In: 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI), pp. 80–85 (2021). IEEE
Song, G., Zhang, J., Mao, D., Chen, G., Pang, C., et al.: A multimodel fusion method for cardiovascular disease detection using ecg. Emergency Medicine International 2022 (2022)
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
