scholarly journals Representation of European hydroclimatic patterns with self-organizing maps

The Holocene ◽  
2020 ◽  
Vol 30 (8) ◽  
pp. 1155-1162 ◽  
Author(s):  
Yannis Markonis ◽  
Filip Strnad
Keyword(s):  
Iberian Peninsula ◽  
Classification Scheme ◽  
Strongly Correlated ◽  
Oceanic Circulation ◽  
Self Organizing Maps ◽  
European Continent ◽  
Spatial Properties ◽  
Hydroclimatic Variability ◽  
Linear Technique ◽  
Self Organizing

Self-organizing maps provide a powerful, non-linear technique of dimensionality reduction that can be used to identify clusters with similar attributes. Here, they were constructed from a 1000-year-long gridded palaeoclimatic dataset, namely the Old World Drought Atlas, to detect regions of homogeneous hydroclimatic variability across the European continent. A classification scheme of 10 regions was found to describe most efficiently the spatial properties of Europe’s hydroclimate. These regions were mainly divided into a northern and a southern subset, linked together with a northwest-to-southeast orientation. Further analysis of the classification scheme with complex networks confirmed the divergence between the northern and southern components of European hydroclimate, also revealing that is not strongly correlated to the Iberian Peninsula. On the contrary, the region covering the British Isles, France and Germany appeared to be linked to both branches, implying links of hydroclimate with atmospheric/oceanic circulation.

An automated satellite cloud classification scheme using self‐organizing maps: Alternative ISCCP weather states

2016 ◽  
Vol 121 (21) ◽  
pp. 13,009-13,030 ◽  
Author(s):  
Adrian J. McDonald ◽  
John J. Cassano ◽  
Ben Jolly ◽  
Simon Parsons ◽  
Alex Schuddeboom
Keyword(s):  
Classification Scheme ◽  
Self Organizing Maps ◽  
Cloud Classification ◽  
Self Organizing

scholarly journals Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Francesco Di Maio ◽  
Roberta Rossetti ◽  
Enrico Zio
Keyword(s):  
Dynamic Systems ◽  
Classification Scheme ◽  
Computational Cost ◽  
Relevant Information ◽  
Ensemble Of Classifiers ◽  
Self Organizing Maps ◽  
Near Misses ◽  
Prime Implicants ◽  
Self Organizing

Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs), and Prime Implicants (PIs). To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs) whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers), for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG).

A Psychographic Segmentation of Kuwaiti Travelers Using Self-Organizing Maps

2019 ◽  
Vol 24 (1) ◽  
pp. 87-92 ◽  
Author(s):  
Yvette Reisinger ◽  
Mohamed M. Mostafa ◽  
John P. Hayes
Keyword(s):  
Self Organizing Maps ◽  
Self Organizing

Self Organizing Maps, Pattern Recognition and Financial Crises

2011 ◽  
Author(s):  
Sylvain Barthelemy ◽  
Pascal Devaux ◽  
Francois Faure ◽  
Matthieu Pautonnier
Keyword(s):  
Pattern Recognition ◽  
Financial Crises ◽  
Self Organizing Maps ◽  
Self Organizing

Using self organizing maps to analyze larval fish assemblage vertical dynamics through environmental-ontogenetic gradients

2021 ◽  
pp. 107410
Author(s):  
I. Álvarez ◽  
J.S. Font-Muñoz ◽  
I. Hernández-Carrasco ◽  
C. Díaz-Gil ◽  
P.M. Salgado-Hernanz ◽  
...  
Keyword(s):  
Fish Assemblage ◽  
Larval Fish ◽  
Self Organizing Maps ◽  
Self Organizing

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.

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

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