Improving Ocean Forecasting Using Deep Learning and Numerical Model Integration

Choi, Youngjin; Park, Youngmin; Hwang, Jee Youn; Jeong, Kijune; Kim, Euihyun

doi:10.3390/jmse10040450

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Computer vision and other DL models can successfully be used to detect numerous types of ocean objects, such as seagrass meadows in a plethora of ocean datasets (Moniruzzaman et al, 2017), some of the specific application include deep water sound processing to identify sources of sound pollution or detect specific marine species (Mishachandar and Vairamuthu, 2021) and to detect and classify fish call types in the northern Gulf of Mexico (Waddell et al, 2021). Satellite ocean data, such as what we discussed above, is also amenable to Deep Learning analysis (Ducournau and Fablet, 2016) including for ocean data forecasting (Choi et al, 2022). Deep Learning approaches also show significant promise for climate change modeling of ocean data such as wave energy forecasting (Bento et al, 2021), as well as sea surface temperature patterns to identify ocean extremes (Prochaska et al, 2021).…”

Section: Machine Learning and Its Application In Gulf Of Mexicomentioning

confidence: 99%

The Gulf of Mexico in trouble: Big data solutions to climate change science

Sunkara

McKenna²,

Kar³

et al. 2023

Front. Mar. Sci.

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The latest technological advancements in the development and production of sensors have led to their increased usage in marine science, thus expanding data volume and rates within the field. The extensive data collection efforts to monitor and maintain the health of marine environments supports the efforts in data driven learning, which can help policy makers in making effective decisions. Machine learning techniques show a lot of promise for improving the quality and scope of marine research by detecting implicit patterns and hidden trends, especially in big datasets that are difficult to analyze with traditional methods. Machine learning is extensively used on marine science data collected in various regions, but it has not been applied in a significant way to data generated in the Gulf of Mexico (GOM). Machine learning methods using ocean science data are showing encouraging results and thus are drawing interest from data science researchers and marine scientists to further the research. The purpose of this paper is to review the existing approaches in studying GOM data, the state of the art in machine learning techniques as applied to the GOM, and propose solutions to GOM data problems. We review several issues faced by marine environments in GOM in addition to climate change and its effects. We also present machine learning techniques and methods used elsewhere to address similar problems and propose applications to problems in the GOM. We find that Harmful Algal Blooms (HABs), hypoxia, and sea-level rises have not received as much attention as other climate change problems and within the machine learning literature, the impacts on estuaries and coastal systems, as well as oyster mortality (also major problems for the GOM) have been understudied – we identify those as important areas for improvement. We anticipate this manuscript will act as a baseline for data science researchers and marine scientists to solve problems in the GOM collaboratively and/or independently.

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Section: Machine Learning and Its Application In Gulf Of Mexicomentioning

confidence: 99%

The Gulf of Mexico in trouble: Big data solutions to climate change science

Sunkara

McKenna²,

Kar³

et al. 2023

Front. Mar. Sci.

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“…These properties are very useful in the process of data assimilation (O'Donncha et al, 2018). For example, Choi et al (2022) assimilated deep learning results for spatiotemporal prediction in the ocean to a numerical forecasting system, improving the model's accuracy. Boosting the model means replacing some physical parameterization processes with a machine-learning model.…”

Section: Application Of Ai In Numerical Modelsmentioning

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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“…There are more than 10 kinds of water quality parameters that characterize water quality, but they are not independent of one another. There are always strong or weak coupling relationships between multiple-parameters of water quality [11,12], and these relationships are enhanced in some specific scenarios.…”

Section: Selection Of Auxiliary Variablesmentioning

confidence: 99%

Research on COD Soft Measurement Technology Based on Multi-Parameter Coupling Analysis Method

Zhang¹,

Duan²,

Yi³

et al. 2022

JMSE

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This paper presents a soft measurement technique for COD (Chemical Oxygen Demand) based on the multiparameter coupling analysis method. First, through mechanism analysis and correlation analysis of historical data during the measurement process, water quality parameters, such as hydrogen potential (PH), dissolved oxygen (DO), turbidity (TU), and electrical conductivity (EC), can be used to estimate COD values. To further improve the estimation accuracy of the water quality parameter model, we adopted a modeling method combining a BP neural network and support vector machine, which showed an average relative error of 6.13% and an absolute coefficient of up to 0.9605. Finally, experiments in a lake environment demonstrate that this method shows excellent performance, with highly reliable and accurate prediction results.

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Improving Ocean Forecasting Using Deep Learning and Numerical Model Integration

Cited by 8 publications

References 33 publications

The Gulf of Mexico in trouble: Big data solutions to climate change science

The Gulf of Mexico in trouble: Big data solutions to climate change science

A review of artificial intelligence in marine science

Research on COD Soft Measurement Technology Based on Multi-Parameter Coupling Analysis Method

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