A review of artificial intelligence in marine science
Utilization and exploitation of marine resources by humans have contributed to the growth of marine research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied to maritime research, complementing traditional marine forecasting models and observation te...
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Online Access: | https://doi.org/10.3389/feart.2023.1090185 https://doaj.org/article/5587dc8962b14c2ba176545ae7d1a7e2 |
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ftdoajarticles:oai:doaj.org/article:5587dc8962b14c2ba176545ae7d1a7e2 2023-05-15T18:18:33+02:00 A review of artificial intelligence in marine science Tao Song Cong Pang Boyang Hou Guangxu Xu Junyu Xue Handan Sun Fan Meng 2023-02-01T00:00:00Z https://doi.org/10.3389/feart.2023.1090185 https://doaj.org/article/5587dc8962b14c2ba176545ae7d1a7e2 EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/feart.2023.1090185/full https://doaj.org/toc/2296-6463 2296-6463 doi:10.3389/feart.2023.1090185 https://doaj.org/article/5587dc8962b14c2ba176545ae7d1a7e2 Frontiers in Earth Science, Vol 11 (2023) artificial intelligence marine science ocean observation ocean element forecasting ocean phenomena Science Q article 2023 ftdoajarticles https://doi.org/10.3389/feart.2023.1090185 2023-02-19T01:28:13Z Utilization and exploitation of marine resources by humans have contributed to the growth of marine research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied to maritime research, complementing traditional marine forecasting models and observation techniques to some degree. This article takes the artificial intelligence algorithmic model as its starting point, references several application trials, and methodically elaborates on the emerging research trend of mixing machine learning and physical modeling concepts. This article discusses the evolution of methodologies for the building of ocean observations, the application of artificial intelligence to remote sensing satellites, smart sensors, and intelligent underwater robots, and the construction of ocean big data. We also cover the method of identifying internal waves (IW), heatwaves, El Niño-Southern Oscillation (ENSO), and sea ice using artificial intelligence algorithms. In addition, we analyze the applications of artificial intelligence models in the prediction of ocean components, including physics-driven numerical models, model-driven statistical models, traditional machine learning models, data-driven deep learning models, and physical models combined with artificial intelligence models. This review shows the growth routes of the application of artificial intelligence in ocean observation, ocean phenomena identification, and ocean elements forecasting, with examples and forecasts of their future development trends from several angles and points of view, by categorizing the various uses of artificial intelligence in the ocean sector. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Frontiers in Earth Science 11 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
artificial intelligence marine science ocean observation ocean element forecasting ocean phenomena Science Q |
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artificial intelligence marine science ocean observation ocean element forecasting ocean phenomena Science Q Tao Song Cong Pang Boyang Hou Guangxu Xu Junyu Xue Handan Sun Fan Meng A review of artificial intelligence in marine science |
topic_facet |
artificial intelligence marine science ocean observation ocean element forecasting ocean phenomena Science Q |
description |
Utilization and exploitation of marine resources by humans have contributed to the growth of marine research. As technology progresses, artificial intelligence (AI) approaches are progressively being applied to maritime research, complementing traditional marine forecasting models and observation techniques to some degree. This article takes the artificial intelligence algorithmic model as its starting point, references several application trials, and methodically elaborates on the emerging research trend of mixing machine learning and physical modeling concepts. This article discusses the evolution of methodologies for the building of ocean observations, the application of artificial intelligence to remote sensing satellites, smart sensors, and intelligent underwater robots, and the construction of ocean big data. We also cover the method of identifying internal waves (IW), heatwaves, El Niño-Southern Oscillation (ENSO), and sea ice using artificial intelligence algorithms. In addition, we analyze the applications of artificial intelligence models in the prediction of ocean components, including physics-driven numerical models, model-driven statistical models, traditional machine learning models, data-driven deep learning models, and physical models combined with artificial intelligence models. This review shows the growth routes of the application of artificial intelligence in ocean observation, ocean phenomena identification, and ocean elements forecasting, with examples and forecasts of their future development trends from several angles and points of view, by categorizing the various uses of artificial intelligence in the ocean sector. |
format |
Article in Journal/Newspaper |
author |
Tao Song Cong Pang Boyang Hou Guangxu Xu Junyu Xue Handan Sun Fan Meng |
author_facet |
Tao Song Cong Pang Boyang Hou Guangxu Xu Junyu Xue Handan Sun Fan Meng |
author_sort |
Tao Song |
title |
A review of artificial intelligence in marine science |
title_short |
A review of artificial intelligence in marine science |
title_full |
A review of artificial intelligence in marine science |
title_fullStr |
A review of artificial intelligence in marine science |
title_full_unstemmed |
A review of artificial intelligence in marine science |
title_sort |
review of artificial intelligence in marine science |
publisher |
Frontiers Media S.A. |
publishDate |
2023 |
url |
https://doi.org/10.3389/feart.2023.1090185 https://doaj.org/article/5587dc8962b14c2ba176545ae7d1a7e2 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Frontiers in Earth Science, Vol 11 (2023) |
op_relation |
https://www.frontiersin.org/articles/10.3389/feart.2023.1090185/full https://doaj.org/toc/2296-6463 2296-6463 doi:10.3389/feart.2023.1090185 https://doaj.org/article/5587dc8962b14c2ba176545ae7d1a7e2 |
op_doi |
https://doi.org/10.3389/feart.2023.1090185 |
container_title |
Frontiers in Earth Science |
container_volume |
11 |
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1766195162909769728 |