文献类型：期刊文献

英文题名：An adaptive real-time modular tidal level prediction mechanism based on EMD and Lipschitz quotients method

作者：Yin, Jianchuan[1,2,3];Wang, Huifeng[1];Wang, Nini[4];Wang, Xuegang[5]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524009, 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;[4]Guangdong Ocean Univ, Coll Math & Comp, Zhanjiang 524009, Peoples R China;[5]CCCC Fourth Harbor Engn Inst Co Ltd, Guangzhou 510230, Peoples R China

年份：2023

卷号：289

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001112699100001)、、EI(收录号：20234615059889)、Scopus(收录号：2-s2.0-85176324579)、WOS

基金：The authors would like to thank the editors and anonymous reviewers for their valuable comments and constructive suggestions that led to a substantially improved paper. This study is supported by the National Natural Science Foundation of China under Grants 52271361 and 52231014, the Special Project for Research and Development in Key areas of Guangdong Province under Grant 2021ZDZX1008, the Guangdong Provincial Natural Science Foundation under Grant 2023A1515010684, and the Scientific Research Start -Up Funds of Guangdong Ocean University under Grant 060302132105.

语种：英文

外文关键词：Tidal level prediction; Modular prediction; Adaptive model; Empirical mode decomposition; Lipschitz quotients; Variable neural network

外文摘要：Real-time prediction of tidal level is vital for on-the-spot activities such as marine transportation and ocean surveys. Aiming at the complex characteristics of nonlinearity, time-varying dynamics, and uncertainty generated by celestial bodies' movements and influenced by geographical as well as hydrometeorological factors, an adaptive real-time modular tidal level prediction mechanism is proposed based on empirical mode decomposition (EMD) and Lipschitz quotients method. An adaptive modular tidal level prediction mechanism is proposed by combining the harmonic analysis method with a variable structure neural network. The order of time series decomposition and the prediction input model order of the neural network are automatically determined based on EMD and the Lipschitz quotients method, respectively. The adaptability of the prediction mechanism is further enhanced with the network dimension, hidden units' locations, and connecting parameters of the variable neural network being online adjusted in a sequential learning scheme. While the extraction of harmonic components alleviates the difficulty in prediction, the multi-resolution decomposition of residual series provides further insight into the time-varying tide dynamics caused by environmental disturbances, thus enabling precise predictions for tidal levels. The feasibility and effectiveness of the proposed adaptive modular tidal prediction mechanism are demonstrated based on the real-measured tidal level data.