Basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models

A new method is discussed using neural network models in combination with empirical orthogonal function (EOF) analysis for the basin-scale wind-wave forecast. For the Bay of Bengal region EOF analysis has been performed separately on the significant wave height (SWH) data, zonal (U) and meridional (...

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Autores principales:	Mrinmoyee Bhattacharya, Mourani Sinha
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Empirical orthogonal function analysis Neural network models Backpropagation algorithms Significant wave height Bay of Bengal Geophysics. Cosmic physics QC801-809 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/1ffa929ba2784f24bfc121630baaf95c
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spelling	oai:doaj.org-article:1ffa929ba2784f24bfc121630baaf95c2021-11-20T05:15:29ZBasin scale wind-wave prediction using empirical orthogonal function analysis and neural network models2666-828910.1016/j.ringps.2021.100032https://doaj.org/article/1ffa929ba2784f24bfc121630baaf95c2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666828921000237https://doaj.org/toc/2666-8289A new method is discussed using neural network models in combination with empirical orthogonal function (EOF) analysis for the basin-scale wind-wave forecast. For the Bay of Bengal region EOF analysis has been performed separately on the significant wave height (SWH) data, zonal (U) and meridional (V) components of wind data. For basin scale forecast the dominant principal component (PC) has been subjected to univariate and multivariate neural network models for future predictions. In the univariate approach, only past values of SWH time series are used and in the multivariate approach, U and V time series are used to predict future SWH values. Efficiency in terms of accuracy and speed of four different backpropagation algorithms, namely, Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG) and Fletcher Conjugate Gradient (CGF) have been compared for 1 to 12 multistep ahead time steps and 1 to 13 neurons. After training the models using varied neurons and the PCs, representing the entire basin, the neurons are fixed at which minimum errors are obtained. Further experiments are conducted using the fixed neurons and the PCs for 1 to 12 time steps ahead SWH prediction. Finally independent datasets consisting of normal and cyclonic wind-wave parameters are tested successfully using the above fixed neurons for delays (1 to 12) corresponding to 3 days or 72 h forecast. The novelty of the study lies is the usage of the PCs which represent the entire basin rather than computations at individual locations which are expensive technically and time consuming.Mrinmoyee BhattacharyaMourani SinhaElsevierarticleEmpirical orthogonal function analysisNeural network modelsBackpropagation algorithmsSignificant wave heightBay of BengalGeophysics. Cosmic physicsQC801-809GeologyQE1-996.5ENResults in Geophysical Sciences, Vol 8, Iss , Pp 100032- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Empirical orthogonal function analysis Neural network models Backpropagation algorithms Significant wave height Bay of Bengal Geophysics. Cosmic physics QC801-809 Geology QE1-996.5
spellingShingle	Empirical orthogonal function analysis Neural network models Backpropagation algorithms Significant wave height Bay of Bengal Geophysics. Cosmic physics QC801-809 Geology QE1-996.5 Mrinmoyee Bhattacharya Mourani Sinha Basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models
description	A new method is discussed using neural network models in combination with empirical orthogonal function (EOF) analysis for the basin-scale wind-wave forecast. For the Bay of Bengal region EOF analysis has been performed separately on the significant wave height (SWH) data, zonal (U) and meridional (V) components of wind data. For basin scale forecast the dominant principal component (PC) has been subjected to univariate and multivariate neural network models for future predictions. In the univariate approach, only past values of SWH time series are used and in the multivariate approach, U and V time series are used to predict future SWH values. Efficiency in terms of accuracy and speed of four different backpropagation algorithms, namely, Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG) and Fletcher Conjugate Gradient (CGF) have been compared for 1 to 12 multistep ahead time steps and 1 to 13 neurons. After training the models using varied neurons and the PCs, representing the entire basin, the neurons are fixed at which minimum errors are obtained. Further experiments are conducted using the fixed neurons and the PCs for 1 to 12 time steps ahead SWH prediction. Finally independent datasets consisting of normal and cyclonic wind-wave parameters are tested successfully using the above fixed neurons for delays (1 to 12) corresponding to 3 days or 72 h forecast. The novelty of the study lies is the usage of the PCs which represent the entire basin rather than computations at individual locations which are expensive technically and time consuming.
format	article
author	Mrinmoyee Bhattacharya Mourani Sinha
author_facet	Mrinmoyee Bhattacharya Mourani Sinha
author_sort	Mrinmoyee Bhattacharya
title	Basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models
title_short	Basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models
title_full	Basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models
title_fullStr	Basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models
title_full_unstemmed	Basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models
title_sort	basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/1ffa929ba2784f24bfc121630baaf95c
work_keys_str_mv	AT mrinmoyeebhattacharya basinscalewindwavepredictionusingempiricalorthogonalfunctionanalysisandneuralnetworkmodels AT mouranisinha basinscalewindwavepredictionusingempiricalorthogonalfunctionanalysisandneuralnetworkmodels
_version_	1718419469649313792

Basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models

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