A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics

Faghmous, James H.; Le, Matthew; Uluyol, Muhammed; Kumar, Vipin; Chatterjee, Snigdhansu

doi:10.1109/icdm.2013.162

Cited by 33 publications

(24 citation statements)

References 28 publications

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“…The autonomous eddy identification scheme 64 starts from the simple notion that every eddy should have a single extremum. An extremum is defined as a grid cell whose SLA is greater (maximum) or less (minimum) than its 24 neighbors in a 5×5 neighborhood.…”

Section: Methodsmentioning

confidence: 99%

“…While anticyclonic eddies would have a single maximum surrounded by a closed-contour SLA. This simple observation, that each eddy ought to contain a single extremum, allows us to address CSS11’s limitation of artificially merging nearby eddies 30 , 33 , 52 , 64 . A video showing how our method avoids merging nearby eddies is available at: https://vimeo.com/108387905 .…”

Section: Methodsmentioning

confidence: 99%

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A daily global mesoscale ocean eddy dataset from satellite altimetry

et al. 2015

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Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993–2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A daily global mesoscale ocean eddy dataset from satellite altimetry

et al. 2015

Self Cite

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“…However, in the presence of spatio-temporal objects with amorphous boundaries and their associated uncertainties [25], there is a need to develop pattern mining approaches that can account for the spatial and temporal properties of geoscience data while characterizing objects and events. One such approach has been successfully used for finding spatio-temporal patterns in sea surface height data [36], [37], resulting in the creation of a global catalogue of mesoscale ocean eddies [38]. Another approach for finding anomalous objects buried under the surface of the Earth (e.g., land mines) from radar images was explored in [39], using unsupervised techniques that can work with mediums of varying properties.…”

Section: Characterizing Objects and Eventsmentioning

confidence: 99%

Machine Learning for the Geosciences: Challenges and Opportunities

Karpatne

Ebert‐Uphoff

Ravela

et al. 2019

IEEE Trans. Knowl. Data Eng.

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197

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Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (ML)-that has been widely successful in commercial domains-offers immense potential to contribute to problems in geosciences. However, problems in geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences. We first highlight typical sources of geoscience data and describe their properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the geosciences, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.

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“…They are responsible for the transport and mixing of heat, salt, nutrients, and energy across an ocean or sea [24]. Moreover, they have a significant impact on terrestrial and marine ecosystems [25]. The creation or growth of eddy provides a large amount of food for phytoplankton and provides them with growth opportunity.…”

Section: Case Descriptionmentioning

confidence: 99%

A Statistical Method for Sequential Images–based Process Monitoring

Fattahzadeh

Saghaei

2020

IJE

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Today, with the growth of technology, monitoring processes by the use of video and satellite sensors have been more expanded, due to their rich and valuable information. Recently, some researchers have used sequential images for defect detection because a single image is not sufficient for process monitoring. In this paper, by adding the time dimension to the image-based process monitoring problem, we detect process changes (such as the changes in the size, location, speed, color, etc.). The temporal correlation between the images and the high dimensionality of the data make this a complex problem. To address this, using the sequential images, a statistical approach with RIDGE regression and a Q control chart is proposed to monitor the process. This method can be applied to color and gray images. To validate the proposed method, it was applied to a real case study and was compared to the best methods in literature. The obtained results showed that it was more effective in finding the changes.

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A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics

Cited by 33 publications

References 28 publications

A daily global mesoscale ocean eddy dataset from satellite altimetry

A daily global mesoscale ocean eddy dataset from satellite altimetry

Machine Learning for the Geosciences: Challenges and Opportunities

A Statistical Method for Sequential Images–based Process Monitoring

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