Short-term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samples
Accurate estimation of extreme sea levels caused by storm surges is critical for coastal engineering, particularly during typhoon seasons. Data-driven approaches have emerged as efficient tools for storm surge prediction. This study presents the development of deep learning neural network models to...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2536074 |
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| author | Zhuo Zhang Lu Zhang Songshan Yue Dong Zhang Zhaoyuan Yu Di Hu Peng Chen |
| author_facet | Zhuo Zhang Lu Zhang Songshan Yue Dong Zhang Zhaoyuan Yu Di Hu Peng Chen |
| author_sort | Zhuo Zhang |
| collection | DOAJ |
| description | Accurate estimation of extreme sea levels caused by storm surges is critical for coastal engineering, particularly during typhoon seasons. Data-driven approaches have emerged as efficient tools for storm surge prediction. This study presents the development of deep learning neural network models to predict storm surge levels in the estuarine and coastal waters of the Pearl River Estuary, China, using typhoon characteristics and previous surge data. Unlike traditional single-point neural network models that focus on individual tide stations, this study introduces multipoint models and incorporates a convolutional layer to extract spatial features from tide levels at neighboring stations. Comparative results reveal that multipoint models significantly increase prediction accuracy by integrating spatiotemporal information from surrounding stations. This advantage is particularly evident when forecasting lead times exceed six hours, where multipoint models demonstrate superior accuracy and stability compared with single-point models. Furthermore, this study evaluates the impact of limited training sample sizes on prediction accuracy, offering valuable insights into the data requirements for robust model training. The findings highlight the potential of using multipoint neural network models as effective tools for storm surge prediction, offering increased accuracy and contributing to enhanced coastal risk management and decision-making in the face of extreme weather events. |
| format | Article |
| id | doaj-art-cdbb6f1cb39d463fbdeb50132b6006a4 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-cdbb6f1cb39d463fbdeb50132b6006a42025-08-25T11:28:21ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2536074Short-term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samplesZhuo Zhang0Lu Zhang1Songshan Yue2Dong Zhang3Zhaoyuan Yu4Di Hu5Peng Chen6State Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, People's Republic of ChinaKey Laboratory of Virtual Geographic Environment, (Nanjing Normal University), Ministry of Education, Nanjing, People’s Republic of ChinaState Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, People's Republic of ChinaSchool of Marine Science and Engineering, Nanjing Normal University, Nanjing, People’s Republic of ChinaState Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, People's Republic of ChinaState Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, People's Republic of ChinaPearl River Hydrology and Water Resources Survey Center, Guangzhou, People’s Republic of ChinaAccurate estimation of extreme sea levels caused by storm surges is critical for coastal engineering, particularly during typhoon seasons. Data-driven approaches have emerged as efficient tools for storm surge prediction. This study presents the development of deep learning neural network models to predict storm surge levels in the estuarine and coastal waters of the Pearl River Estuary, China, using typhoon characteristics and previous surge data. Unlike traditional single-point neural network models that focus on individual tide stations, this study introduces multipoint models and incorporates a convolutional layer to extract spatial features from tide levels at neighboring stations. Comparative results reveal that multipoint models significantly increase prediction accuracy by integrating spatiotemporal information from surrounding stations. This advantage is particularly evident when forecasting lead times exceed six hours, where multipoint models demonstrate superior accuracy and stability compared with single-point models. Furthermore, this study evaluates the impact of limited training sample sizes on prediction accuracy, offering valuable insights into the data requirements for robust model training. The findings highlight the potential of using multipoint neural network models as effective tools for storm surge prediction, offering increased accuracy and contributing to enhanced coastal risk management and decision-making in the face of extreme weather events.https://www.tandfonline.com/doi/10.1080/17538947.2025.2536074Deep learningneural networksurge predictionestuarine and coastal watersPearl River Estuary |
| spellingShingle | Zhuo Zhang Lu Zhang Songshan Yue Dong Zhang Zhaoyuan Yu Di Hu Peng Chen Short-term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samples International Journal of Digital Earth Deep learning neural network surge prediction estuarine and coastal waters Pearl River Estuary |
| title | Short-term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samples |
| title_full | Short-term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samples |
| title_fullStr | Short-term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samples |
| title_full_unstemmed | Short-term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samples |
| title_short | Short-term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samples |
| title_sort | short term prediction of storm surges in estuarine and coastal waters via a multipoint deep learning neural network with limited training samples |
| topic | Deep learning neural network surge prediction estuarine and coastal waters Pearl River Estuary |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2536074 |
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