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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/9/11/1257 |
| _version_ | 1797509824564428800 |
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| author | Chih-Chiang Wei |
| author_facet | Chih-Chiang Wei |
| author_sort | Chih-Chiang Wei |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:23:12Z |
| format | Article |
| id | doaj.art-f3c44feebcd04ca89510bd90c50842c8 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T05:23:12Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-f3c44feebcd04ca89510bd90c50842c82023-11-22T23:54:05ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-11-01911125710.3390/jmse9111257Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural NetworksChih-Chiang Wei0Department of Marine Environmental Informatics & Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung 20224, TaiwanNearshore 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.https://www.mdpi.com/2077-1312/9/11/1257wave heightwind fieldconvolution operationrecurrent operationfeature extractiontyphoon |
| spellingShingle | Chih-Chiang Wei Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks Journal of Marine Science and Engineering wave height wind field convolution operation recurrent operation feature extraction typhoon |
| title | 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_short | 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 |
| topic | wave height wind field convolution operation recurrent operation feature extraction typhoon |
| url | https://www.mdpi.com/2077-1312/9/11/1257 |
| work_keys_str_mv | AT chihchiangwei windfeaturesextractedfromweathersimulationsforwindwavepredictionusinghighresolutionneuralnetworks |