文献类型：期刊文献

英文题名：A Hybrid Tidal Prediction Scheme Based on Adaptive Modules Adjustment

作者：Wang, Rui[1];Yin, Jian-chuan[1,2,3];Xu, Dong-xing[1,2,3]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524005, Peoples R China;[2]Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China;[3]Guangdong Prov Engn Res Ctr Ship Intelligence & Sa, Zhanjiang 524088, Peoples R China

年份：2026

卷号：40

期号：1

起止页码：144

外文期刊名：CHINA OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001693256700018)、、EI(收录号：20260820100414)、WOS

基金：This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 52271361 and 52231014), the Natural Science Foundation of Guangdong Province of China (Grant No. 2023A1515010684), and the Special Projects of Key Areas for Colleges and Universities of Guangdong Province (Grant No. 2021ZDZX1008).

语种：英文

外文关键词：tide forecast; harmonic analysis; empirical mode decomposition (EMD); long short-term memory (LSTM); polynomial fitting (PF); adaptive module adjustment (AMA)

外文摘要：Due to complex environmental disturbances, high-precision tidal prediction remains a significant challenge in ocean engineering applications. To address tidal variations characterized by nonlinearity, uncertainty, and time-varying dynamics, a hybrid prediction scheme incorporating adaptive module adjustment (AMA) is proposed. The harmonic analysis method is first applied to model tidal effects induced by the movements of celestial bodies. Subsequently, residual components are decomposed using empirical mode decomposition (EMD), with long short-term memory (LSTM) networks and polynomial fitting (PF) employed to construct the tidal prediction model. The decomposition order for LSTM input time series and the selection of polynomial modules are determined adaptively. Finally, the predictions from harmonic analysis and the ensemble model components are combined to generate the final tidal forecast. Experimental simulations are conducted using observed tidal data from gauges at Canaveral Port and Old Port Tampa. Simulation results demonstrate that the proposed adaptive tidal prediction model outperforms conventional methods in terms of prediction accuracy.