Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data
The development of intelligent oceans requires exploration and an understanding of the various characteristics of the oceans. The emerging Internet of Underwater Things (IoUT) is an extension of the Internet of Things (IoT) to underwater environments, and the ability of IoUT to be combined with deep...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/3/446 |
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| author | Sai Wang Guoping Fu Yongduo Song Jing Wen Tuanqi Guo Hongjin Zhang Tuantuan Wang |
| author_facet | Sai Wang Guoping Fu Yongduo Song Jing Wen Tuanqi Guo Hongjin Zhang Tuantuan Wang |
| author_sort | Sai Wang |
| collection | DOAJ |
| description | The development of intelligent oceans requires exploration and an understanding of the various characteristics of the oceans. The emerging Internet of Underwater Things (IoUT) is an extension of the Internet of Things (IoT) to underwater environments, and the ability of IoUT to be combined with deep learning technologies is a powerful technology for realizing intelligent oceans. The underwater acoustic (UWA) communication network is essential to IoUT. The thermocline with drastic temperature and density variations can significantly limit the connectivity and communication performance between IoUT nodes. To more accurately capture the complexity and variability of ocean remote sensing data, we first sample and analyze ocean remote sensing datasets and provide sufficient evidence to validate the temporal redundancy properties of the data. We propose an innovative deep learning approach called Ocean-Mixer. This approach consists of three modules: an embedding module, a mixer module, and a prediction module. The embedding module first processes the location and attribute information of the ocean water and then passes it to the subsequent modules. In the mixing module, we apply a temporal decomposition strategy to eliminate redundant information and capture temporal and channel features through a self-attention mechanism and a multilayer perceptron (MLP). The prediction module ultimately discerns and integrates the temporal and channel relationships and interactions among various ocean features, ensuring precise forecasting. Numerous experiments on ocean temperature and salinity datasets show that Mixer-Ocean performs well in improving the accuracy of time series prediction. Mixer-Ocean is designed to support multi-step prediction and capture the changes in the ocean environment over a long period, thus facilitating efficient management and timely decision-making for innovative ocean-oriented applications, which has far-reaching significance for developing and conserving marine resources. |
| first_indexed | 2024-04-24T18:06:39Z |
| format | Article |
| id | doaj.art-161a8b52e77d4c7dbf1f33b79fa78bb7 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-04-24T18:06:39Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-161a8b52e77d4c7dbf1f33b79fa78bb72024-03-27T13:49:17ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-03-0112344610.3390/jmse12030446Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing DataSai Wang0Guoping Fu1Yongduo Song2Jing Wen3Tuanqi Guo4Hongjin Zhang5Tuantuan Wang6State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, ChinaSchool of Ecology and Environment, Hainan University, Haikou 570228, ChinaThe development of intelligent oceans requires exploration and an understanding of the various characteristics of the oceans. The emerging Internet of Underwater Things (IoUT) is an extension of the Internet of Things (IoT) to underwater environments, and the ability of IoUT to be combined with deep learning technologies is a powerful technology for realizing intelligent oceans. The underwater acoustic (UWA) communication network is essential to IoUT. The thermocline with drastic temperature and density variations can significantly limit the connectivity and communication performance between IoUT nodes. To more accurately capture the complexity and variability of ocean remote sensing data, we first sample and analyze ocean remote sensing datasets and provide sufficient evidence to validate the temporal redundancy properties of the data. We propose an innovative deep learning approach called Ocean-Mixer. This approach consists of three modules: an embedding module, a mixer module, and a prediction module. The embedding module first processes the location and attribute information of the ocean water and then passes it to the subsequent modules. In the mixing module, we apply a temporal decomposition strategy to eliminate redundant information and capture temporal and channel features through a self-attention mechanism and a multilayer perceptron (MLP). The prediction module ultimately discerns and integrates the temporal and channel relationships and interactions among various ocean features, ensuring precise forecasting. Numerous experiments on ocean temperature and salinity datasets show that Mixer-Ocean performs well in improving the accuracy of time series prediction. Mixer-Ocean is designed to support multi-step prediction and capture the changes in the ocean environment over a long period, thus facilitating efficient management and timely decision-making for innovative ocean-oriented applications, which has far-reaching significance for developing and conserving marine resources.https://www.mdpi.com/2077-1312/12/3/446thermoclinedeep learningInternet of Underwater Things (IoUT)multi-step predictiontemperature predictionsalinity prediction |
| spellingShingle | Sai Wang Guoping Fu Yongduo Song Jing Wen Tuanqi Guo Hongjin Zhang Tuantuan Wang Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data Journal of Marine Science and Engineering thermocline deep learning Internet of Underwater Things (IoUT) multi-step prediction temperature prediction salinity prediction |
| title | Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data |
| title_full | Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data |
| title_fullStr | Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data |
| title_full_unstemmed | Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data |
| title_short | Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data |
| title_sort | ocean mixer a deep learning approach for multi step prediction of ocean remote sensing data |
| topic | thermocline deep learning Internet of Underwater Things (IoUT) multi-step prediction temperature prediction salinity prediction |
| url | https://www.mdpi.com/2077-1312/12/3/446 |
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