scholarly journals Patterns of ocean current variability on the West Florida Shelf using the self-organizing map

2005 ◽  
Vol 110 (C6) ◽  
Author(s):  
Yonggang Liu
Keyword(s):  
The Self ◽  
Ocean Current ◽  
Self Organizing Map ◽  
West Florida Shelf ◽  
The West ◽  
Florida Shelf ◽  
Self Organizing

scholarly journals Current Patterns on the West Florida Shelf from Joint Self-Organizing Map Analyses of HF Radar and ADCP Data

2007 ◽  
Vol 24 (4) ◽  
pp. 702-712 ◽  
Author(s):  
Yonggang Liu ◽  
Robert H. Weisberg ◽  
Lynn K. Shay
Keyword(s):  
Hf Radar ◽  
Ocean Current ◽  
Self Organizing Map ◽  
West Florida Shelf ◽  
The West ◽  
Florida Shelf ◽  
Buoyancy Forcing ◽  
Temporal Structures ◽  
Self Organizing

Abstract To assess the spatial structures and temporal evolutions of distinct physical processes on the West Florida Shelf, patterns of ocean current variability are extracted from a joint HF radar and ADCP dataset acquired from August to September 2003 using Self-Organizing Map (SOM) analyses. Three separate ocean–atmosphere frequency bands are considered: semidiurnal, diurnal, and subtidal. The currents in the semidiurnal band are relatively homogeneous in space, barotropic, clockwise polarized, and have a neap-spring modulation consistent with semidiurnal tides. The currents in the diurnal band are less homogeneous, more baroclinic, and clockwise polarized, consistent with a combination of diurnal tides and near-inertial oscillations. The currents in the subtidal frequency band are stronger and with more complex patterns consistent with wind and buoyancy forcing. The SOM is shown to be a useful technique for extracting ocean current patterns with dynamically distinctive spatial and temporal structures sampled by HF radar and supporting in situ measurements.

scholarly journals Sea Surface Temperature Patterns on the West Florida Shelf Using Growing Hierarchical Self-Organizing Maps

2006 ◽  
Vol 23 (2) ◽  
pp. 325-338 ◽  
Author(s):  
Yonggang Liu ◽  
Robert H. Weisberg ◽  
Ruoying He
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Self Organizing Map ◽  
West Florida Shelf ◽  
The West ◽  
Florida Shelf ◽  
Network Analyses ◽  
Sst Anomaly ◽  
Self Organizing

Abstract Neural network analyses based on the self-organizing map (SOM) and the growing hierarchical self-organizing map (GHSOM) are used to examine patterns of the sea surface temperature (SST) variability on the West Florida Shelf from time series of daily SST maps from 1998 to 2002. Four characteristic SST patterns are extracted in the first-layer GHSOM array: winter and summer season patterns, and two transitional patterns. Three of them are further expanded in the second layer, yielding more detailed structures in these seasons. The winter pattern is one of low SST, with isotherms aligned approximately along isobaths. The summer pattern is one of high SST distributed in a horizontally uniform manner. The spring transition includes a midshelf cold tongue. Similar analyses performed on SST anomaly data provide further details of these seasonally varying patterns. It is demonstrated that the GHSOM analysis is more effective in extracting the inherent SST patterns than the widely used EOF method. The underlying patterns in a dataset can be visualized in the SOM array in the same form as the original data, while they can only be expressed in anomaly form in the EOF analysis. Some important features, such as asymmetric SST anomaly patterns of winter/summer and cold/warm tongues, can be revealed by the SOM array but cannot be identified in the lowest mode EOF patterns. Also, unlike the EOF or SOM techniques, the hierarchical structure in the input data can be extracted by the GHSOM analysis.

USGS field activity 09FSH02 on the west Florida shelf, Gulf of Mexico, in August 2009

Data Series ◽  
2009 ◽  
Author(s):  
Lisa L. Robbins ◽  
Paul O. Knorr ◽  
Xuewu Liu ◽  
Robert H. Byrne ◽  
Ellen A. Raabe
Keyword(s):  
Gulf Of Mexico ◽  
West Florida Shelf ◽  
The West ◽  
Florida Shelf ◽  
Field Activity

USGS Field Activities 11CEV01 and 11CEV02 on the West Florida Shelf, Gulf of Mexico, in January and February 2011

Data Series ◽  
10.3133/ds711 ◽  
2014 ◽  
Author(s):  
Lisa L. Robbins ◽  
Paul O. Knorr ◽  
Kendra L. Daly ◽  
Carl A. Taylor
Keyword(s):  
Gulf Of Mexico ◽  
West Florida Shelf ◽  
The West ◽  
Florida Shelf

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

High-Frequency Radar Mapping of Surface Currents Using WERA

2007 ◽  
Vol 24 (3) ◽  
pp. 484-503 ◽  
Author(s):  
Lynn K. Shay ◽  
Jorge Martinez-Pedraja ◽  
Thomas M. Cook ◽  
Brian K. Haus ◽  
Robert H. Weisberg
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Tidal Currents ◽  
Tropical Storm ◽  
Surface Velocity ◽  
Surface Currents ◽  
West Florida Shelf ◽  
The West ◽  
Florida Shelf ◽  
East West

Abstract A dual-station high-frequency Wellen Radar (WERA), transmitting at 16.045 MHz, was deployed along the west Florida shelf in phased array mode during the summer of 2003. A 33-day, continuous time series of radial and vector surface current fields was acquired starting on 23 August ending 25 September 2003. Over a 30-min sample interval, WERA mapped coastal ocean currents over an ≈40 km × 80 km footprint with a 1.2-km horizontal resolution. A total of 1628 snapshots of the vector surface currents was acquired, with only 70 samples (4.3%) missing from the vector time series. Comparisons to subsurface measurements from two moored acoustic Doppler current profilers revealed RMS differences of 1 to 5 cm s−1 for both radial and Cartesian current components. Regression analyses indicated slopes close to unity with small biases between surface and subsurface measurements at 4-m depth in the east–west (u) and north–south (υ) components, respectively. Vector correlation coefficients were 0.9 with complex phases of −3° and 5° at EC4 (20-m isobath) and NA2 (25-m isobath) moorings, respectively. Complex surface circulation patterns were observed that included tidal and wind-driven currents over the west Florida shelf. Tidal current amplitudes were 4 to 5 cm s−1 for the diurnal and semidiurnal constituents. Vertical structure of these tidal currents indicated that the semidiurnal components were predominantly barotropic whereas diurnal tidal currents had more of a baroclinic component. Tidal currents were removed from the observed current time series and were compared to the 10-m adjusted winds at a surface mooring. Based on these time series comparisons, regression slopes were 0.02 to 0.03 in the east–west and north–south directions, respectively. During Tropical Storm Henri’s passage on 5 September 2003, cyclonically rotating surface winds forced surface velocities of more than 35 cm s−1 as Henri made landfall north of Tampa Bay, Florida. These results suggest that the WERA measured the surface velocity well under weak to tropical storm wind conditions.

VISUAL APPROACH TO SUPERVISED VARIABLE SELECTION BY SELF-ORGANIZING MAP

2005 ◽  
Vol 15 (01n02) ◽  
pp. 101-110 ◽  
Author(s):  
TIMO SIMILÄ ◽  
SAMPSA LAINE
Keyword(s):  
Variable Selection ◽  
The Self ◽  
Data Sets ◽  
Self Organizing Map ◽  
Robust Method ◽  
Relevant Variables ◽  
Visual Approach ◽  
Predefined Criterion ◽  
Target Data ◽  
Self Organizing

Practical data analysis often encounters data sets with both relevant and useless variables. Supervised variable selection is the task of selecting the relevant variables based on some predefined criterion. We propose a robust method for this task. The user manually selects a set of target variables and trains a Self-Organizing Map with these data. This sets a criterion to variable selection and is an illustrative description of the user's problem, even for multivariate target data. The user also defines another set of variables that are potentially related to the problem. Our method returns a subset of these variables, which best corresponds to the description provided by the Self-Organizing Map and, thus, agrees with the user's understanding about the problem. The method is conceptually simple and, based on experiments, allows an accessible approach to supervised variable selection.

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