Application of machine learning in ocean data
In recent years, machine learning has become a hot research method in various fields and has been applied to every aspect of our life, providing an intelligent solution to problems that could not be solved or difficult to be solved before. Machine learning is driven by data. It learns from a part of...
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| Vydáno v: | Multimedia systems Ročník 29; číslo 3; s. 1815 - 1824 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2023
Springer Nature B.V |
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| ISSN: | 0942-4962, 1432-1882 |
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| Abstract | In recent years, machine learning has become a hot research method in various fields and has been applied to every aspect of our life, providing an intelligent solution to problems that could not be solved or difficult to be solved before. Machine learning is driven by data. It learns from a part of the input data and builds a model. The model is used to predict and analyze another part of the data to get the results people want. With the continuous advancement of ocean observation technology, the amount of ocean data and data dimensions are rising sharply. The use of traditional data analysis methods to analyze massive amounts of data has revealed many shortcomings. The development of machine learning has solved these shortcomings. Nowadays, the use of machine learning technology to analyze and apply ocean data becomes the focus of scientific research. This method has important practical and long-term significance for protecting the ocean environment, predicting ocean elements, exploring the unknown, and responding to extreme weather. This paper focuses on the analysis of the state of the art and specific practices of machine learning in ocean data, review the application examples of machine learning in various fields such as ocean sound source identification and positioning, ocean element prediction, ocean biodiversity monitoring, and deep-sea resource monitoring. We also point out some constraints that still exist in the research and put forward the future development direction and application prospects. |
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| AbstractList | In recent years, machine learning has become a hot research method in various fields and has been applied to every aspect of our life, providing an intelligent solution to problems that could not be solved or difficult to be solved before. Machine learning is driven by data. It learns from a part of the input data and builds a model. The model is used to predict and analyze another part of the data to get the results people want. With the continuous advancement of ocean observation technology, the amount of ocean data and data dimensions are rising sharply. The use of traditional data analysis methods to analyze massive amounts of data has revealed many shortcomings. The development of machine learning has solved these shortcomings. Nowadays, the use of machine learning technology to analyze and apply ocean data becomes the focus of scientific research. This method has important practical and long-term significance for protecting the ocean environment, predicting ocean elements, exploring the unknown, and responding to extreme weather. This paper focuses on the analysis of the state of the art and specific practices of machine learning in ocean data, review the application examples of machine learning in various fields such as ocean sound source identification and positioning, ocean element prediction, ocean biodiversity monitoring, and deep-sea resource monitoring. We also point out some constraints that still exist in the research and put forward the future development direction and application prospects. |
| Author | Lou, Ranran Su, Tianyun Dang, Shuping Lv, Zhihan Li, Xinfang |
| Author_xml | – sequence: 1 givenname: Ranran surname: Lou fullname: Lou, Ranran organization: School of Data Science and Software Engineering, Qingdao University (QDU) – sequence: 2 givenname: Zhihan orcidid: 0000-0003-2525-3074 surname: Lv fullname: Lv, Zhihan email: lvzhihan@gmail.com organization: School of Data Science and Software Engineering, Qingdao University (QDU) – sequence: 3 givenname: Shuping surname: Dang fullname: Dang, Shuping organization: Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST) – sequence: 4 givenname: Tianyun surname: Su fullname: Su, Tianyun organization: Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Marine Data and Information Center, The First Institute of Oceanography, MNR, National Engineering Laboratory for Integrated, Aero-Space-Ground-Ocean Big Data Application Technology – sequence: 5 givenname: Xinfang surname: Li fullname: Li, Xinfang organization: Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Marine Data and Information Center, The First Institute of Oceanography, MNR, National Engineering Laboratory for Integrated, Aero-Space-Ground-Ocean Big Data Application Technology |
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| Keywords | Ocean Data Ocean data Machine learning |
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