A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
Accurate prediction of the height of ocean waves is critical to ensuring maritime safety, optimizing offshore operations, and mitigating coastal hazards. To improve the accuracy of ocean wave height prediction, we developed a hybrid model that integrates decomposition and deep learning. The approach...
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/10/5517 |
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| author | Yelian Sun Longkun Yu Dandan Zhu |
| author_facet | Yelian Sun Longkun Yu Dandan Zhu |
| author_sort | Yelian Sun |
| collection | DOAJ |
| description | Accurate prediction of the height of ocean waves is critical to ensuring maritime safety, optimizing offshore operations, and mitigating coastal hazards. To improve the accuracy of ocean wave height prediction, we developed a hybrid model that integrates decomposition and deep learning. The approach combines Fourier transform, seasonal and trend decomposition using Loess, and various deep learning models, which can more accurately capture the periodicity, trends, and random fluctuations. The trend, seasonality, and residual components are predicted using the LSTM model, SARIMAX, and 1D-CNN, respectively. The mean square error of the model prediction was calculated to be 0.0087 and the root mean square error was 0.0935. The results show that the hybrid model outperforms the other methods compared in our experiments. This model can accurately predict ocean wave heights and provides a reference for predicting time-series data with seasonal fluctuations. |
| format | Article |
| id | doaj-art-b493ecd592734d5fa602fc98fdad4b35 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b493ecd592734d5fa602fc98fdad4b352025-08-20T03:47:53ZengMDPI AGApplied Sciences2076-34172025-05-011510551710.3390/app15105517A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height PredictionYelian Sun0Longkun Yu1Dandan Zhu2School of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaSchool of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaSchool of Advanced Manufacturing, Nanchang University, Nanchang 330031, ChinaAccurate prediction of the height of ocean waves is critical to ensuring maritime safety, optimizing offshore operations, and mitigating coastal hazards. To improve the accuracy of ocean wave height prediction, we developed a hybrid model that integrates decomposition and deep learning. The approach combines Fourier transform, seasonal and trend decomposition using Loess, and various deep learning models, which can more accurately capture the periodicity, trends, and random fluctuations. The trend, seasonality, and residual components are predicted using the LSTM model, SARIMAX, and 1D-CNN, respectively. The mean square error of the model prediction was calculated to be 0.0087 and the root mean square error was 0.0935. The results show that the hybrid model outperforms the other methods compared in our experiments. This model can accurately predict ocean wave heights and provides a reference for predicting time-series data with seasonal fluctuations.https://www.mdpi.com/2076-3417/15/10/5517deep learningocean wave heightdata decomposition |
| spellingShingle | Yelian Sun Longkun Yu Dandan Zhu A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction Applied Sciences deep learning ocean wave height data decomposition |
| title | A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction |
| title_full | A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction |
| title_fullStr | A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction |
| title_full_unstemmed | A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction |
| title_short | A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction |
| title_sort | hybrid deep learning model based on fft stl decomposition for ocean wave height prediction |
| topic | deep learning ocean wave height data decomposition |
| url | https://www.mdpi.com/2076-3417/15/10/5517 |
| work_keys_str_mv | AT yeliansun ahybriddeeplearningmodelbasedonfftstldecompositionforoceanwaveheightprediction AT longkunyu ahybriddeeplearningmodelbasedonfftstldecompositionforoceanwaveheightprediction AT dandanzhu ahybriddeeplearningmodelbasedonfftstldecompositionforoceanwaveheightprediction AT yeliansun hybriddeeplearningmodelbasedonfftstldecompositionforoceanwaveheightprediction AT longkunyu hybriddeeplearningmodelbasedonfftstldecompositionforoceanwaveheightprediction AT dandanzhu hybriddeeplearningmodelbasedonfftstldecompositionforoceanwaveheightprediction |