ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Observation
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1668178
This article is part of the Research TopicIntegrating Unmanned Platforms and Deep Learning Technologies for Enhanced Ocean Observation and Risk Mitigation in Ocean EngineeringView all articles
A Sequential Coastal Current Prediction Approach Based on Hierarchical Decomposition
Provisionally accepted
- Guangdong Ocean University, Zhanjiang, China
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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.
Keywords: Coastal current prediction, Hierarchical decomposition, Sequential learning, time series prediction, Time series decomposition
Received: 17 Jul 2025; Accepted: 19 Aug 2025.
Copyright: © 2025 Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Nini Wang, Guangdong Ocean University, Zhanjiang, China
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