文献类型：期刊文献

英文题名：A sequential coastal current prediction approach based on hierarchical decomposition

作者：Wang, Nini[1]

机构：[1]Guangdong Ocean Univ, Coll Math & Comp, Zhanjiang, Peoples R China

年份：2025

卷号：12

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001575821400001)、、WOS

基金：The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Natural Science Foundation of China under Grants 52271361 and 52231014, the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under Grant 2021ZDZX1008, the Natural Science Foundation of Guangdong Province of China under Grant 2023A1515010684, and the Program for Scientific Research Start-Up Funds of Guangdong Ocean University.

语种：英文

外文关键词：coastal current prediction; hierarchical decomposition; sequential learning; time series prediction; time series decomposition

外文摘要：Precise prediction of coastal tidal current is essential for the efficient operation of tidal power generation, coastal engineering and maritime activities. To excavate the useful information in coastal current movement thus improving the accuracy of coastal current prediction, a real-time sequential mechanism for coastal current prediction is proposed based on a data reconstruction scheme. The reconstruction decomposes the coastal current time series by taking both advantage of the autonomy of the empirical mode decomposition and the arbitrariness of the discrete wavelet transformation, and the decomposed components are identified and predicted respectively by radial basis function networks with variable structure whose hidden units' locations can be adjusted in real-time. To improve the adaptivity and rapidity of the prediction mechanism, the Lipschitz quotients method is employed to determine the prediction system structure, with a sliding data window serving as system dynamics observer. Coastal current prediction simulation is conducted using the measurement data of the tidal gauge of Cumberland Sound, USA and the results validated the effectiveness of the proposed mechanism in respect of prediction accuracy and processing speed.