Application of the principal component analysis on geochemical data: a case study in the basement complex of Southern Ilesa area, Nigeria

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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Assessment of water quality using principal component analysis: a case study of the Marrecas stream basin in Brazil

Environmental Technology ◽

10.1080/09593330.2020.1754922 ◽

2020 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Alexandre Teixeira de Souza ◽

Lucas Augusto T. X. Carneiro ◽

Osmar Pereira da Silva Junior ◽

Sérgio Luís de Carvalho ◽

Juliana Heloisa Pinê Américo-Pinheiro

Keyword(s):

Water Quality ◽

Principal Component Analysis ◽

Principal Component ◽

Component Analysis

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Evaluation of the plant landscape suitability in mountain parks based on principal component analysis—a case study of guiyang city

Agronomy Journal ◽

10.1002/agj2.20463 ◽

2020 ◽

Author(s):

Ting Yang ◽

Xiu‐Rong Wang

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Landscape Suitability ◽

Guiyang City

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SVD-based principal component analysis of geochemical data

Open Chemistry ◽

10.2478/bf02475200 ◽

2005 ◽

Vol 3 (4) ◽

pp. 731-741 ◽

Cited By ~ 11

Author(s):

Petr Praus

Keyword(s):

Principal Component Analysis ◽

Euclidean Distance ◽

Principal Component ◽

Component Analysis ◽

Data Matrix ◽

Geochemical Data ◽

Average Group ◽

Testing Data ◽

Value Decomposition ◽

Geochemical Variables

AbstractPrincipal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2% of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration.The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.

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