A Modular Tide Level Prediction Method Based on a NARX Neural Network

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: as...

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Autores principales:	Wenhao Wu, Lianbo Li, Jianchuan Yin, Wenyu Lyu, Wenjun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	GA-BP neural network harmonic analysis WNN modular prediction NARX neural network tide prediction Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e7e0b7ebc78f418a8388c29d3ac276ba
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spelling	oai:doaj.org-article:e7e0b7ebc78f418a8388c29d3ac276ba2021-11-18T00:07:48ZA Modular Tide Level Prediction Method Based on a NARX Neural Network2169-353610.1109/ACCESS.2021.3124250https://doaj.org/article/e7e0b7ebc78f418a8388c29d3ac276ba2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9594794/https://doaj.org/toc/2169-3536Tide 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.Wenhao WuLianbo LiJianchuan YinWenyu LyuWenjun ZhangIEEEarticleGA-BP neural networkharmonic analysisWNNmodular predictionNARX neural networktide predictionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147416-147429 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GA-BP neural network harmonic analysis WNN modular prediction NARX neural network tide prediction Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	GA-BP neural network harmonic analysis WNN modular prediction NARX neural network tide prediction Electrical engineering. Electronics. Nuclear engineering TK1-9971 Wenhao Wu Lianbo Li Jianchuan Yin Wenyu Lyu Wenjun Zhang A Modular Tide Level Prediction Method Based on a NARX Neural Network
description	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.
format	article
author	Wenhao Wu Lianbo Li Jianchuan Yin Wenyu Lyu Wenjun Zhang
author_facet	Wenhao Wu Lianbo Li Jianchuan Yin Wenyu Lyu Wenjun Zhang
author_sort	Wenhao Wu
title	A Modular Tide Level Prediction Method Based on a NARX Neural Network
title_short	A Modular Tide Level Prediction Method Based on a NARX Neural Network
title_full	A Modular Tide Level Prediction Method Based on a NARX Neural Network
title_fullStr	A Modular Tide Level Prediction Method Based on a NARX Neural Network
title_full_unstemmed	A Modular Tide Level Prediction Method Based on a NARX Neural Network
title_sort	modular tide level prediction method based on a narx neural network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/e7e0b7ebc78f418a8388c29d3ac276ba
work_keys_str_mv	AT wenhaowu amodulartidelevelpredictionmethodbasedonanarxneuralnetwork AT lianboli amodulartidelevelpredictionmethodbasedonanarxneuralnetwork AT jianchuanyin amodulartidelevelpredictionmethodbasedonanarxneuralnetwork AT wenyulyu amodulartidelevelpredictionmethodbasedonanarxneuralnetwork AT wenjunzhang amodulartidelevelpredictionmethodbasedonanarxneuralnetwork AT wenhaowu modulartidelevelpredictionmethodbasedonanarxneuralnetwork AT lianboli modulartidelevelpredictionmethodbasedonanarxneuralnetwork AT jianchuanyin modulartidelevelpredictionmethodbasedonanarxneuralnetwork AT wenyulyu modulartidelevelpredictionmethodbasedonanarxneuralnetwork AT wenjunzhang modulartidelevelpredictionmethodbasedonanarxneuralnetwork
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A Modular Tide Level Prediction Method Based on a NARX Neural Network

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