OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environments
IntroductionSalt spray formation and migration in hot and humid marine environments have a significant impact on marine engineering and equipment maintenance. Accurately predicting these phenomena is crucial for reducing corrosion damage. Traditional research methodologies primarily utilize statisti...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1518050/full |
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| author | Chuan Chen Ganxin Jie Jun Wang Shouhe Wang Xiaonong Xu |
| author_facet | Chuan Chen Ganxin Jie Jun Wang Shouhe Wang Xiaonong Xu |
| author_sort | Chuan Chen |
| collection | DOAJ |
| description | IntroductionSalt spray formation and migration in hot and humid marine environments have a significant impact on marine engineering and equipment maintenance. Accurately predicting these phenomena is crucial for reducing corrosion damage. Traditional research methodologies primarily utilize statistical models or physics-based simulations. Although these approaches yield satisfactory results within controlled conditions, they often encounter limitations in accurately capturing the complexity and variability inherent to marine environments. These methods struggle to capture the spatiotemporal dependencies of salt spray formation and migration. Moreover, they are typically difficult to apply in real-time and lack the ability to handle large-scale, dynamic data.MethodsThis study aims to address this issue by proposing the OceanLSTM model, which combines the temporal modeling capabilities of xLSTM with a spatial attention mechanism to capture the spatiotemporal relationships between complex environmental variables, thereby improving the accuracy of salt spray predictions.ResultsThe experiments used several representative marine environment datasets, including the NOAA and Marine Aerosol datasets. The experimental results demonstrate that OceanLSTM significantly outperforms traditional models in evaluation metrics such as accuracy and F1-score, especially on datasets with strong spatiotemporal dependencies.DiscussionThis research provides a more precise and efficient tool for future marine environment monitoring and corrosion prediction, offering important practical applications. |
| format | Article |
| id | doaj-art-c51c5c55266e4e399e10e003d71cb3a7 |
| institution | OA Journals |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-c51c5c55266e4e399e10e003d71cb3a72025-08-20T02:16:15ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-05-011210.3389/fmars.2025.15180501518050OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environmentsChuan Chen0Ganxin Jie1Jun Wang2Shouhe Wang3Xiaonong Xu4China National Electric Apparatus Research Institute Co., Ltd., State Key Laboratory of Environmental Adaptability for Industrial Products, Guangzhou, ChinaChina National Electric Apparatus Research Institute Co., Ltd., State Key Laboratory of Environmental Adaptability for Industrial Products, Guangzhou, ChinaChina National Electric Apparatus Research Institute Co., Ltd., State Key Laboratory of Environmental Adaptability for Industrial Products, Guangzhou, ChinaChina National Electric Apparatus Research Institute Co., Ltd., State Key Laboratory of Environmental Adaptability for Industrial Products, Guangzhou, ChinaThe College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, ChinaIntroductionSalt spray formation and migration in hot and humid marine environments have a significant impact on marine engineering and equipment maintenance. Accurately predicting these phenomena is crucial for reducing corrosion damage. Traditional research methodologies primarily utilize statistical models or physics-based simulations. Although these approaches yield satisfactory results within controlled conditions, they often encounter limitations in accurately capturing the complexity and variability inherent to marine environments. These methods struggle to capture the spatiotemporal dependencies of salt spray formation and migration. Moreover, they are typically difficult to apply in real-time and lack the ability to handle large-scale, dynamic data.MethodsThis study aims to address this issue by proposing the OceanLSTM model, which combines the temporal modeling capabilities of xLSTM with a spatial attention mechanism to capture the spatiotemporal relationships between complex environmental variables, thereby improving the accuracy of salt spray predictions.ResultsThe experiments used several representative marine environment datasets, including the NOAA and Marine Aerosol datasets. The experimental results demonstrate that OceanLSTM significantly outperforms traditional models in evaluation metrics such as accuracy and F1-score, especially on datasets with strong spatiotemporal dependencies.DiscussionThis research provides a more precise and efficient tool for future marine environment monitoring and corrosion prediction, offering important practical applications.https://www.frontiersin.org/articles/10.3389/fmars.2025.1518050/fullsalt spray predictionmarine environmentxLSTMspatial attentionspatiotemporal modeling |
| spellingShingle | Chuan Chen Ganxin Jie Jun Wang Shouhe Wang Xiaonong Xu OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environments Frontiers in Marine Science salt spray prediction marine environment xLSTM spatial attention spatiotemporal modeling |
| title | OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environments |
| title_full | OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environments |
| title_fullStr | OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environments |
| title_full_unstemmed | OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environments |
| title_short | OceanLSTM: xLSTM with spatial attention for salt spray formation and migration prediction in marine hot-humid environments |
| title_sort | oceanlstm xlstm with spatial attention for salt spray formation and migration prediction in marine hot humid environments |
| topic | salt spray prediction marine environment xLSTM spatial attention spatiotemporal modeling |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1518050/full |
| work_keys_str_mv | AT chuanchen oceanlstmxlstmwithspatialattentionforsaltsprayformationandmigrationpredictioninmarinehothumidenvironments AT ganxinjie oceanlstmxlstmwithspatialattentionforsaltsprayformationandmigrationpredictioninmarinehothumidenvironments AT junwang oceanlstmxlstmwithspatialattentionforsaltsprayformationandmigrationpredictioninmarinehothumidenvironments AT shouhewang oceanlstmxlstmwithspatialattentionforsaltsprayformationandmigrationpredictioninmarinehothumidenvironments AT xiaonongxu oceanlstmxlstmwithspatialattentionforsaltsprayformationandmigrationpredictioninmarinehothumidenvironments |