Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks

Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave forecasts during typhoon periods are necessary. The p...

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Autor principal:	Chih-Chiang Wei
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wave height wind field convolution operation recurrent operation feature extraction typhoon Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/f3c44feebcd04ca89510bd90c50842c8
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id	oai:doaj.org-article:f3c44feebcd04ca89510bd90c50842c8
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spelling	oai:doaj.org-article:f3c44feebcd04ca89510bd90c50842c82021-11-25T18:04:50ZWind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks10.3390/jmse91112572077-1312https://doaj.org/article/f3c44feebcd04ca89510bd90c50842c82021-11-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1257https://doaj.org/toc/2077-1312Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave forecasts during typhoon periods are necessary. The purpose of this study is to develop artificial intelligence (AI)-based techniques for forecasting wind–wave processes near coastal areas during typhoons. The proposed integrated models employ combined a numerical weather prediction (NWP) model and AI techniques, namely numerical (NUM)-AI-based wind–wave prediction models. This hybrid model comprising VGGNNet and High-Resolution Network (HRNet) was integrated with recurrent-based gated recurrent unit (GRU). Termed mVHR_GRU, this model was constructed using a convolutional layer for extracting features from spatial images with high-to-low resolution and a recurrent GRU model for time series prediction. To investigate the potential of mVHR_GRU for wind–wave prediction, VGGNet, HRNet, and Two-Step Wind-Wave Prediction (TSWP) were selected as benchmark models. The coastal waters in northeast Taiwan were the study area. The length of the forecast horizon was from 1 to 6 h. The mVHR_GRU model outperformed the HR_GRU, VGGNet, and TSWP models according to the error indicators. The coefficient of mVHR_GRU efficiency improved by 13% to 18% and by 13% to 15% at the Longdong and Guishandao buoys, respectively. In addition, in a comparison of the NUM–AI-based model and a numerical model simulating waves nearshore (SWAN), the SWAN model generated greater errors than the NUM–AI-based model. The results of the NUM–AI-based wind–wave prediction model were in favorable accordance with the observed results, indicating the feasibility of the established model in processing spatial data.Chih-Chiang WeiMDPI AGarticlewave heightwind fieldconvolution operationrecurrent operationfeature extractiontyphoonNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1257, p 1257 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wave height wind field convolution operation recurrent operation feature extraction typhoon Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
spellingShingle	wave height wind field convolution operation recurrent operation feature extraction typhoon Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Chih-Chiang Wei Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks
description	Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave forecasts during typhoon periods are necessary. The purpose of this study is to develop artificial intelligence (AI)-based techniques for forecasting wind–wave processes near coastal areas during typhoons. The proposed integrated models employ combined a numerical weather prediction (NWP) model and AI techniques, namely numerical (NUM)-AI-based wind–wave prediction models. This hybrid model comprising VGGNNet and High-Resolution Network (HRNet) was integrated with recurrent-based gated recurrent unit (GRU). Termed mVHR_GRU, this model was constructed using a convolutional layer for extracting features from spatial images with high-to-low resolution and a recurrent GRU model for time series prediction. To investigate the potential of mVHR_GRU for wind–wave prediction, VGGNet, HRNet, and Two-Step Wind-Wave Prediction (TSWP) were selected as benchmark models. The coastal waters in northeast Taiwan were the study area. The length of the forecast horizon was from 1 to 6 h. The mVHR_GRU model outperformed the HR_GRU, VGGNet, and TSWP models according to the error indicators. The coefficient of mVHR_GRU efficiency improved by 13% to 18% and by 13% to 15% at the Longdong and Guishandao buoys, respectively. In addition, in a comparison of the NUM–AI-based model and a numerical model simulating waves nearshore (SWAN), the SWAN model generated greater errors than the NUM–AI-based model. The results of the NUM–AI-based wind–wave prediction model were in favorable accordance with the observed results, indicating the feasibility of the established model in processing spatial data.
format	article
author	Chih-Chiang Wei
author_facet	Chih-Chiang Wei
author_sort	Chih-Chiang Wei
title	Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks
title_short	Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks
title_full	Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks
title_fullStr	Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks
title_full_unstemmed	Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks
title_sort	wind features extracted from weather simulations for wind-wave prediction using high-resolution neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f3c44feebcd04ca89510bd90c50842c8
work_keys_str_mv	AT chihchiangwei windfeaturesextractedfromweathersimulationsforwindwavepredictionusinghighresolutionneuralnetworks
_version_	1718411717287870464

Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks

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