Prediction of the Marine Dynamic Environment for Arctic Ice-Based Buoys Using Historical Profile Data
In this paper, the time-series model is used to predict whether an ocean buoy is about to be inside a vortex. Marine buoys are an important tool for collecting ocean data and studying ocean dynamics, climate change, and ecosystem health. A vortex is an important ocean dynamic process. If we can pred...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1003 |
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| _version_ | 1849705209403539456 |
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| author | Jingzi Zhu Yu Luo Tao Li Yanhai Gan Junyu Dong |
| author_facet | Jingzi Zhu Yu Luo Tao Li Yanhai Gan Junyu Dong |
| author_sort | Jingzi Zhu |
| collection | DOAJ |
| description | In this paper, the time-series model is used to predict whether an ocean buoy is about to be inside a vortex. Marine buoys are an important tool for collecting ocean data and studying ocean dynamics, climate change, and ecosystem health. A vortex is an important ocean dynamic process. If we can predict that a buoy is about to enter a vortex, we can automatically adjust the buoy’s sampling frequency to better observe the vortex’s structure and development. To address this requirement, based on the profile data, including latitude and longitude, temperature, and salinity, collected by 56 buoys in the Arctic Ocean from 2014 to 2023, this paper uses the TSMixer time-series model to predict whether an ocean buoy is about to be inside a vortex. The TSMixer model effectively captures the spatio-temporal characteristics of multivariate time series through time-mixing and feature-mixing mechanisms, and the accuracy of the model reaches 84.6%. The proposed model is computationally efficient and has a low memory footprint, which is suitable for real-time applications and provides accurate prediction support for marine monitoring. |
| format | Article |
| id | doaj-art-ebfc90d9ba5c4977902f807d769c3b10 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-ebfc90d9ba5c4977902f807d769c3b102025-08-20T03:16:32ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-01136100310.3390/jmse13061003Prediction of the Marine Dynamic Environment for Arctic Ice-Based Buoys Using Historical Profile DataJingzi Zhu0Yu Luo1Tao Li2Yanhai Gan3Junyu Dong4Haide College, Ocean University of China, 238 Songling Road, Qingdao 266100, ChinaSchool of Mathematical Sciences, Ocean University of China, 238 Songling Road, Qingdao 266100, ChinaCollege of Oceanic and Atmospheric Sciences, Ocean University of China, 238 Songling Road, Qingdao 266100, ChinaFaculty of Information Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, ChinaFaculty of Information Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, ChinaIn this paper, the time-series model is used to predict whether an ocean buoy is about to be inside a vortex. Marine buoys are an important tool for collecting ocean data and studying ocean dynamics, climate change, and ecosystem health. A vortex is an important ocean dynamic process. If we can predict that a buoy is about to enter a vortex, we can automatically adjust the buoy’s sampling frequency to better observe the vortex’s structure and development. To address this requirement, based on the profile data, including latitude and longitude, temperature, and salinity, collected by 56 buoys in the Arctic Ocean from 2014 to 2023, this paper uses the TSMixer time-series model to predict whether an ocean buoy is about to be inside a vortex. The TSMixer model effectively captures the spatio-temporal characteristics of multivariate time series through time-mixing and feature-mixing mechanisms, and the accuracy of the model reaches 84.6%. The proposed model is computationally efficient and has a low memory footprint, which is suitable for real-time applications and provides accurate prediction support for marine monitoring.https://www.mdpi.com/2077-1312/13/6/1003ocean buoyprofilevortex predictiontime-series modelTSMixer |
| spellingShingle | Jingzi Zhu Yu Luo Tao Li Yanhai Gan Junyu Dong Prediction of the Marine Dynamic Environment for Arctic Ice-Based Buoys Using Historical Profile Data Journal of Marine Science and Engineering ocean buoy profile vortex prediction time-series model TSMixer |
| title | Prediction of the Marine Dynamic Environment for Arctic Ice-Based Buoys Using Historical Profile Data |
| title_full | Prediction of the Marine Dynamic Environment for Arctic Ice-Based Buoys Using Historical Profile Data |
| title_fullStr | Prediction of the Marine Dynamic Environment for Arctic Ice-Based Buoys Using Historical Profile Data |
| title_full_unstemmed | Prediction of the Marine Dynamic Environment for Arctic Ice-Based Buoys Using Historical Profile Data |
| title_short | Prediction of the Marine Dynamic Environment for Arctic Ice-Based Buoys Using Historical Profile Data |
| title_sort | prediction of the marine dynamic environment for arctic ice based buoys using historical profile data |
| topic | ocean buoy profile vortex prediction time-series model TSMixer |
| url | https://www.mdpi.com/2077-1312/13/6/1003 |
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