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AuthorGao, Ruobin
AuthorLi, Ruilin
AuthorHu, Minghui
AuthorSuganthan, Ponnuthurai Nagaratnam
AuthorYuen, Kum Fai
Available date2023-02-08T08:23:17Z
Publication Date2023-01-01
Publication NameEngineering Applications of Artificial Intelligence
Identifierhttp://dx.doi.org/10.1016/j.engappai.2022.105535
CitationGao, R., Li, R., Hu, M., Suganthan, P. N., & Yuen, K. F. (2023). Significant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning. Engineering Applications of Artificial Intelligence, 117, 105535.‏
ISSN09521976
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85141229894&origin=inward
URIhttp://hdl.handle.net/10576/39803
AbstractThe reliable control of wave energy devices highly relies on the forecasts of wave heights. However, the dynamic characteristics and significant fluctuation of waves’ historical data pose challenges to precise predictions. Neural networks offer a promising solution to forecast the wave heights by extracting meaningful features from historical observations. This paper proposes a novel hybrid random vector functional link network with the ensemble and deep learning benefits. Hierarchical stacks of hidden layers are constructed to enforce the deep representations of the time series. Individual output layers follow all enhancement layers to adopt ensemble learning. A neuron pruning strategy is proposed to remove the noisy information from the random features and boost the network's performance. Besides, the proposed network is further utilized to forecast the additive and multiplicative residuals from the ARIMA method. Finally, the ensemble of additive-ARIMA-edRVFL, multiplicative-ARIMA-edRVFL, and edRVFL achieves the best average rankings around two for three forecasting horizons. The proposed ensemble achieves an average ranking of 1.33 on four-hours ahead of forecasting in terms of root mean square error and mean absolute scaled error. Extensive experiments are conducted on twelve time series of the significant wave height. The comparative results demonstrate the superiority of the proposed model over other state-of-the-art methods. The source codes are available on https://github.com/P-N-Suganthan/CODES.
Languageen
PublisherElsevier Ltd
SubjectBayesian optimization
Deep learning
Ocean energy
Random vector functional link
Time series forecasting
TitleSignificant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning
TypeArticle
Volume Number117
dc.accessType Abstract Only


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