Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height
Predicting significant wave height (SWH) is significant for coastal energy evaluation and utilization, port construction, and shipping planning. It has been reported that SWH is difficult to forecast for the complex marine conditions and chaos in nature. Current methods either require reliable prior...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9917531/ |
| _version_ | 1797990908800532480 |
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| author | Xiangjun Yu Yarong Liu Zhiming Sun Pan Qin |
| author_facet | Xiangjun Yu Yarong Liu Zhiming Sun Pan Qin |
| author_sort | Xiangjun Yu |
| collection | DOAJ |
| description | Predicting significant wave height (SWH) is significant for coastal energy evaluation and utilization, port construction, and shipping planning. It has been reported that SWH is difficult to forecast for the complex marine conditions and chaos in nature. Current methods either require reliable prior information or reach the upper limit of prediction accuracy. To this end, this paper proposes a wavelet-based residual network to predict SWH with high accuracy. First, the time-series data of wave-related factors collected by the ocean buoy station is decomposed using the wavelet transformation. Then, the transformation results are used as the inputs to train the residual neural network. Finally, the data obtained from the NOAA’s National Data Buoy Center is used to prove the outperformed prediction accuracy of the proposed method. The analysis results suggested that wavelet transformation can improve the prediction performance of the neural network, and the proposed model achieves better performance compared with several other deep neural network schemes. |
| first_indexed | 2024-04-11T08:43:02Z |
| format | Article |
| id | doaj.art-543f03b8872a4ecba9fcf0786a3d05d1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T08:43:02Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-543f03b8872a4ecba9fcf0786a3d05d12022-12-22T04:34:02ZengIEEEIEEE Access2169-35362022-01-011011002611003310.1109/ACCESS.2022.32143179917531Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave HeightXiangjun Yu0Yarong Liu1Zhiming Sun2Pan Qin3https://orcid.org/0000-0003-0545-3044Department of Military Oceanography and Hydrography, Dalian Naval Academy, Dalian, ChinaSchool of Control Science and Engineering, Dalian University of Technology, Dalian, ChinaSchool of Control Science and Engineering, Dalian University of Technology, Dalian, ChinaSchool of Control Science and Engineering, Dalian University of Technology, Dalian, ChinaPredicting significant wave height (SWH) is significant for coastal energy evaluation and utilization, port construction, and shipping planning. It has been reported that SWH is difficult to forecast for the complex marine conditions and chaos in nature. Current methods either require reliable prior information or reach the upper limit of prediction accuracy. To this end, this paper proposes a wavelet-based residual network to predict SWH with high accuracy. First, the time-series data of wave-related factors collected by the ocean buoy station is decomposed using the wavelet transformation. Then, the transformation results are used as the inputs to train the residual neural network. Finally, the data obtained from the NOAA’s National Data Buoy Center is used to prove the outperformed prediction accuracy of the proposed method. The analysis results suggested that wavelet transformation can improve the prediction performance of the neural network, and the proposed model achieves better performance compared with several other deep neural network schemes.https://ieeexplore.ieee.org/document/9917531/Convolution networkdata miningocean wave time seriesResNetwavelet decomposition |
| spellingShingle | Xiangjun Yu Yarong Liu Zhiming Sun Pan Qin Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height IEEE Access Convolution network data mining ocean wave time series ResNet wavelet decomposition |
| title | Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height |
| title_full | Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height |
| title_fullStr | Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height |
| title_full_unstemmed | Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height |
| title_short | Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height |
| title_sort | wavelet based resnet a deep learning model for prediction of significant wave height |
| topic | Convolution network data mining ocean wave time series ResNet wavelet decomposition |
| url | https://ieeexplore.ieee.org/document/9917531/ |
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