Recent Developments in Artificial Intelligence in Oceanography

Xu, Guangjun; Han, Guoqing; Bethel, Brandon J.; Xie, Wenhong; Zhou, Siyang

doi:10.34133/2022/9870950

Cited by 41 publications

(21 citation statements)

References 185 publications

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“…Large amplitudes, long crests, and long propagation distances characterize internal waves (IW) (Zhang et al, 2022). Internal waves occur at all ocean depths and differ from other waves in that they play an essential role in transmitting the energy of mesoscale and large-scale motions (Dong et al, 2022a). In addition, internal waves cause disturbances at the ocean's surface, which can cause problems for maritime transport.…”

Section: Internal Wavesmentioning

confidence: 99%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

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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.

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Section: Internal Wavesmentioning

confidence: 99%

A review of artificial intelligence in marine science

Song

Pang²,

Hou³

et al. 2023

Front. Earth Sci.

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“…The use of data‐driven methods in oceanography offers several advantages, such as flexibility, high generalizability, and ease of modeling, and has proven to work effectively in many applications. Nevertheless, the paucity of interpretability inherent in data‐driven models poses a formidable obstacle to their extensive implementation within the domain of marine science (Dong et al., 2022; S. Wang, Teng, & Perdikaris, 2021). Data‐driven models typically rely solely on the data for learning the relationships between inputs and outputs (Doshi‐Velez & Kim, 2017; Guo et al., 2016), without considering the dynamic rules.…”

Section: Introductionmentioning

confidence: 99%

Physics‐Informed Deep Operator Learning Based on Reduced‐Order Modeling for Retrieving the Ocean Interior Density From the Surface

Chen,

Liu,

Yuan

et al. 2024

JGR Oceans

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Exploring methods to reconstruct the ocean interior from surface data is a crucial focus in the study of ocean processes and phenomena due to the shortage of subsurface and deep‐sea data. Nonetheless, the existing methods predominantly concentrate on either data‐driven or dynamical methodologies, with limited exploration of integrating the strengths of both approaches. To combine the advantages of these two methods for reconstructing the subsurface density field from surface data, a novel dynamics‐constrained deep operator learning network based on reduced‐order model is proposed. Encoding the mean‐squared residuals of the reduced‐order equation along with the mean‐squared error between the network outputs and targets into the loss function effectively merges the dynamical and data constraints during the training process. This integration makes the network outputs and inputs approximately satisfy a specific form of the equation, allowing for interpretability, and once the network is well‐trained, rapid reconstruction evaluation can be performed. The reduced‐order equation is established by the Galerkin projection of quasi‐geostrophic equation onto the low‐dimensional subspace identified via reduced‐basis, which explains the vertical variation of ocean density. The developed model can tackle the challenge of directly measuring subsurface potential vorticity and predicting subsurface density. Evaluation is conducted using simulation data from the Max‐Planck‐Institute ocean model, indicating that it can offer precise estimations, outperforms the purely data‐driven algorithm presented in the paper and the interior plus surface quasi‐geostrophic method, and enables model sharing across different regions.

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“…At present, it is still a great challenge to accurately predict ENSO more than 1 year in advance using traditional physics-based dynamic models (10,(16)(17)(18). Fortunately, recent advances in deep learning (DL) algorithms and their innovative applications to earth sciences provide a promising way to improve modeling of natural weather and climate phenomena (19)(20)(21).…”

Section: Introductionmentioning

confidence: 99%

A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions

Zhou

2023

Sci. Adv.

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Large biases and uncertainties remain in real-time predictions of El Niño–Southern Oscillation (ENSO) using process-based dynamical models; recent advances in data-driven deep learning algorithms provide a promising mean to achieve superior skill in the tropical Pacific sea surface temperature (SST) modeling. Here, a specific self-attention–based neural network model is developed for ENSO predictions based on the much sought-after Transformer model, named 3D-Geoformer, which is used to predict three-dimensional (3D) upper-ocean temperature anomalies and wind stress anomalies. This purely data-driven and time-space attention-enhanced model achieves surprisingly high correlation skills for Niño 3.4 SST anomaly predictions made 18 months in advance and initiated beginning in boreal spring. Further, sensitivity experiments demonstrate that the 3D-Geoformer model can depict the evolution of upper-ocean temperature and the coupled ocean-atmosphere dynamics following the Bjerknes feedback mechanism during ENSO cycles. Such successful realizations of the self-attention–based model in ENSO predictions indicate its great potential for multidimensional spatiotemporal modeling in geoscience.

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Recent Developments in Artificial Intelligence in Oceanography

Cited by 41 publications

References 185 publications

A review of artificial intelligence in marine science

A review of artificial intelligence in marine science

Physics‐Informed Deep Operator Learning Based on Reduced‐Order Modeling for Retrieving the Ocean Interior Density From the Surface

A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions

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