A deep learning model for joint prediction of three-dimensional ocean temperature, salinity and flow fields
文献类型：会议论文
| 作者 | Jin QL(金乾隆)1,2,3 ; Tian Y(田宇)2,3; Song SM(桑启明)1,2,3; Liu SJ(刘世杰)2,3; Yu JC(俞建成)2,3; Wang XH(王晓辉)2,3
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| 出版日期 | 2021 |
| 会议日期 | July 15-17, 2021 |
| 会议地点 | Dalian, China |
| 关键词 | ocean prediction deep learning convolutional LSTM adaptive ocean sensing marine vehicles |
| 页码 | 573-577 |
| 英文摘要 | Temporal prediction of three-dimensional spatial fields of ocean temperature, salinity and flow is important for a number of civilian and military applications. For accurate prediction of regional ocean environments, combing an accurate dynamic ocean model with optimized in-situ observations of ocean states provided by a network of marine vehicles (such as unmanned surface vehicles, autonomous underwater vehicles, and underwater gliders) is an important approach. To realize fast and accurate three-dimensional spatial prediction of regional ocean environments via ocean models and fast optimization of ocean observation strategies of marine vehicles guided by ocean models, this paper proposes a data driven dynamic model for temporal prediction of three-dimensional regional ocean environments. The proposed model is based on the deep learning method, and is developed based on the Convolutional-LSTM (long short-term memory) network. The feature of the proposed model is that the correlation of temperature, salinity and flow is considered and implemented in the neural network, with which the joint prediction of three-dimensional ocean temperature, salinity and flow fields is achieved. Data set form a numerical ocean model is used to conduct the training of the model and the results demonstrate that the proposed model could provide more accurate prediction than implementing prediction of single ocean temperature, salinity or flow field. The proposed model could be integrated with marine vehicles to form an accurate, high-resolution three-dimensional regional ocean prediction and fast-response adaptive sensing system. |
| 产权排序 | 1 |
| 会议录 | 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE)
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-6654-3576-5 |
| 源URL | [http://ir.sia.cn/handle/173321/29903]  |
| 专题 | 海洋机器人卓越创新中心 |
| 通讯作者 | Jin QL(金乾隆) |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China |
| 推荐引用方式 GB/T 7714 | Jin QL,Tian Y,Song SM,et al. A deep learning model for joint prediction of three-dimensional ocean temperature, salinity and flow fields[C]. 见:. Dalian, China. July 15-17, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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