scholarly journals Hydrogeochemical Characterization and Its Seasonal Changes of Groundwater Based on Self-Organizing Maps

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3065
Author(s):  
Chu Wu ◽  
Xiong Wu ◽  
Chuiyu Lu ◽  
Qingyan Sun ◽  
Xin He ◽  
...  
Keyword(s):  
Seasonal Variability ◽  
Seasonal Changes ◽  
Saturation Index ◽  
Sustainable Use ◽  
Social Economy ◽  
Water Source ◽  
Groundwater Chemistry ◽  
Hydrochemical Facies ◽  
Self Organizing Maps ◽  
Self Organizing

Water resources are scarce in arid or semiarid areas; groundwater is an important water source to maintain residents’ lives and the social economy; and identifying the hydrogeochemical characteristics of groundwater and its seasonal changes is a prerequisite for sustainable use and protection of groundwater. This study takes the Hongjiannao Basin as an example, and the Piper diagram, the Gibbs diagram, the Gaillardet diagram, the Chlor-alkali index, the saturation index, and the ion ratio were used to analyze the hydrogeochemical characteristics of groundwater. Meanwhile, based on self-organizing maps (SOM), quantification error (QE), topological error (TE), and the K-means algorithm, groundwater chemical data analysis was carried out to explore its seasonal variability. The results show that (1) the formation of groundwater chemistry in the study area was controlled by water–rock interactions and cation exchange, and the hydrochemical facies were HCO3-Ca type, HCO3-Na type, and Cl-Na type. (2) Groundwater chemical composition was mainly controlled by silicate weathering and carbonate dissolution, and the dissolution of halite, gypsum, and fluorite dominated the contribution of ions, while most dolomite and calcite were in a precipitated state or were reactive minerals. (3) All groundwater samples in wet and dry seasons were divided into five clusters, and the hydrochemical facies of clusters 1, 2, and 3 were HCO3-Ca type; cluster 4 was HCO3-Na type; and cluster 5 was Cl-Na type. (4) Thirty samples changed in the same clusters, and the groundwater chemistry characteristics of nine samples showed obvious seasonal variability, while the seasonal changes of groundwater hydrogeochemical characteristics were not significant.

scholarly journals Classification of groundwater chemistry in Shimabara, using self-organizing maps

2016 ◽  
Vol 48 (3) ◽  
pp. 840-850 ◽  
Author(s):  
Kei Nakagawa ◽  
Hiroki Amano ◽  
Akira Kawamura ◽  
Ronny Berndtsson
Keyword(s):  
Water Supply ◽  
Groundwater Chemistry ◽  
Polluted Water ◽  
Chemical Components ◽  
Self Organizing Maps ◽  
Water Supply Management ◽  
Sampling Locations ◽  
Private And Public ◽  
Almost All ◽  
Self Organizing

Shimabara City in Nagasaki Prefecture, Japan, is located on a volcanic peninsula that has abundant groundwater. Almost all public water supplies use groundwater in this region. For this reason, understanding groundwater characteristics is a pre-requisite for proper water supply management. Thus, we investigated the groundwater chemistry characteristics in Shimabara by use of self-organizing maps (SOMs). The input to SOM was concentrations of eight major groundwater chemical components, namely Cl−, NO3−, SO42–, HCO3−, Na+, K+, Mg2+, and Ca2+ collected at 36 sampling locations. The locations constituted private and public water supply wells, springs, and a river sampled from April 2012 to May 2015. Results showed that depending on the chemistry, surface water and groundwater could be classified into five main clusters displaying unique patterns. Further, the five clusters could be divided into two major water types, namely, nitrate- and non-polluted water. According to Stiff and Piper trilinear diagrams, the nitrate-polluted water represented Ca-(SO4 + NO3) (calcium sulfate nitrate) type, while the non-polluted water was classified as Ca-HCO3 (calcium bicarbonate) type. This indicates that recharging rain water in the upstream areas is polluted by agricultural activities in the mid-slope areas of Shimabara.

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.

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