The Role of Artificial Intelligence Algorithms in Marine Scientific Research
The study of marine science is vital to the survival and development of humanity. On the one hand, the oceans act as a global climate regulator, supplying 70% of oxygen and 87.5% of water vapor to the atmosphere while storing large amounts of heat (Petterson et al., 2021). On the other hand, the oceans act as an important part of the global physical system, in which changes in mass-energy, biological, and geological processes can have a significant impact on marine and terrestrial life (Du et al., 2021). However, lacking knowledge of important areas such as the deep sea and polar regions, humans cannot yet decipher certain specific phenomena and patterns in the oceans.
Artificial Intelligence (AI) algorithms are trained on mathematical models with a specific structure using a large amount of statistical data to obtain a fitter that contains the statistical features inherent in the training data. It can be applied to solve optimization problems. As a result, AI algorithms have been very successful in a number of scientific fields, such as autonomous driving (Khan et al., 2021), medical imaging (Hickman et al., 2022), geophysics (Yu and Ma, 2021), and nanoscience (Jiang et al., 2022).
As marine scientific research enters a new era of intelligence and constantly improving marine data, AI can effectively tap into the potential information contained in vast amounts of data. As a result, it is also gaining more and more attention from marine researchers (Logares et al., 2021). The integration of AI technology with traditional models to improve marine safety has also been proven (Khayyam et al., 2020). In addition, data processing problems in marine pollution (Agarwala, 2021), wind and wave energy (Gu and Li, 2022) research can be solved using AI algorithms.
Therefore, this paper reports on the prospects of applying AI algorithmic methods in marine scientific research, mainly monitoring marine biodiversity, deep-sea resource modelling, and predicting SST, tide level, sea ice, and climate. In addition, the paper discusses the current problems of AI algorithms in processing marine data and building predictive models.
