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Application of machine learning in ocean data

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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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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant no. 61902203, Key Research and Development Plan—Major Scientific and Technological Innovation Projects of ShanDong Province (2019JZZY020101).

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Authors and Affiliations

  1. School of Data Science and Software Engineering, Qingdao University (QDU), Qingdao, China

    Ranran Lou & Zhihan Lv

  2. Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao, China

    Tianyun Su & Xinfang Li

  3. Marine Data and Information Center, The First Institute of Oceanography, MNR, Qingdao, China

    Tianyun Su & Xinfang Li

  4. National Engineering Laboratory for Integrated, Aero-Space-Ground-Ocean Big Data Application Technology, Qingdao, China

    Tianyun Su & Xinfang Li

  5. Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Shuping Dang

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Lou, R., Lv, Z., Dang, S. et al. Application of machine learning in ocean data. Multimedia Systems 29, 1815–1824 (2023). https://doi.org/10.1007/s00530-020-00733-x

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