文献类型：期刊文献

英文题名：A Modular Tide Level Prediction Method Based on a NARX Neural Network

作者：Wu, Wenhao[1];Li, Lianbo[1];Yin, Jianchuan[2];Lyu, Wenyu[3];Zhang, Wenjun[1]

机构：[1]Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China;[2]Guangdong Ocean Univ, Maritime Coll, Zhanjiang 524088, Peoples R China;[3]Dalian Maritime Univ, Sch Foreign Languages, Dalian 116026, Peoples R China

年份：2021

卷号：9

起止页码：147416

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000716681600001)、、EI(收录号：20214511129463)、Scopus(收录号：2-s2.0-85118586167)、WOS

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB1600602; in part by the National Natural Science Foundation of China under Grant 51879024; in part by the Liaoning Natural Science Planning Project Foundation under Grant 20180550283, Grant 2020-HYLH-27, and Grant 201801704; in part by the Dalian Maritime University Teaching Reform Project under Grant 2020Y04; in part by the Transportation Education Scienti~c Research Project of China Institute of Communications Education under Grant JTYB20-03 and Grant JTYB20-09; and in part by the Fundamental Research Funds for the Central Universities under Grant 3132021133.

语种：英文

外文关键词：Tides; Biological neural networks; Predictive models; Harmonic analysis; Neural networks; Mathematical models; Data models; GA-BP neural network; harmonic analysis; WNN; modular prediction; NARX neural network; tide prediction

外文摘要：Tide variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. To improve the accuracy of tide prediction, a modular tide level prediction model (HA-NARX) is proposed. This model divides tide data into two parts: astronomical tide data affected by celestial tide-generating forces and nonastronomical tide data affected by various environmental factors. Final tide prediction results are obtained using a nonlinear autoregressive exogenous model (NARX) neural network combined with harmonic analysis (HA) data. To verify the feasibility of the model, tide data under different climatic and geographical conditions are used to simulate the prediction of tide levels, and the results are compared with those of traditional HA, the genetic algorithm-back propagation (GA-BP) neural network and the wavelet neural network (WNN). The results show that the greater the influence of meteorological factors on tides, the more obvious is the improvement in accuracy and stability of HA-NARX prediction results compared to traditional models, with the highest prediction accuracy improvement of 234%. The proposed model not only has a simple structure but can also effectively improve the stability and accuracy of tide prediction.