scholarly journals Main patterns of the geomagnetic field: A case study using principal component analysis

Physicae ◽  
2015 ◽  
Vol 11 (11) ◽  
pp. 1-11
Author(s):  
Virginia Klausner ◽  
Odim Mendes ◽  
Margarete Oliveira Domingues ◽  
Andres Reinaldo Rodrigues Papa
2009 ◽  
Author(s):  
Virgínia Klausner ◽  
Odim Mendes Jr ◽  
Andrés R. R. Papa ◽  
Margarete Oliveira Domingues

Physicae ◽  
2015 ◽  
Vol 11 (11) ◽  
pp. 1-11
Author(s):  
Virginia Klausner ◽  
Odim Mendes ◽  
Margarete Oliveira Domingues ◽  
Andres Reinaldo Rodrigues Papa

2010 ◽  
Vol 4 (1-2) ◽  
pp. 239-247 ◽  
Author(s):  
Emmanuel A. Ariyibi ◽  
Samuel L. Folami ◽  
Bankole D. Ako ◽  
Taye R. Ajayi ◽  
Adebowale O. Adelusi

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 437 ◽  
Author(s):  
Ana Marín Celestino ◽  
Diego Martínez Cruz ◽  
Elena Otazo Sánchez ◽  
Francisco Gavi Reyes ◽  
David Vásquez Soto

Author(s):  
Petr Praus

In this chapter the principals and applications of principal component analysis (PCA) applied on hydrological data are presented. Four case studies showed the possibility of PCA to obtain information about wastewater treatment process, drinking water quality in a city network and to find similarities in the data sets of ground water quality results and water-related images. In the first case study, the composition of raw and cleaned wastewater was characterised and its temporal changes were displayed. In the second case study, drinking water samples were divided into clusters in consistency with their sampling localities. In the case study III, the similar samples of ground water were recognised by the calculation of cosine similarity, the Euclidean and Manhattan distances. In the case study IV, 32 water-related images were transformed into a large image matrix whose dimensionality was reduced by PCA. The images were clustered using the PCA scatter plots.


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