Application of Remote Sensing Technique to Detect Copper Mineral based Principal Component Analysis and Band Ratio Methods. A Case Study: Laocai Province, Vietnam

2015 ◽  
Vol 7 (3) ◽  
pp. 137-145 ◽  
Author(s):  
Le Hung Trinh ◽  
◽  
Danh Tuyen Vu
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Band Ratio ◽  
Copper Mineral ◽  
Remote Sensing Technique

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

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
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Geochemical Data ◽  
Basement Complex

scholarly journals Groundwater Quality Assessment: An Improved Approach to K-Means Clustering, Principal Component Analysis and Spatial Analysis: A Case Study

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 437 ◽  
Author(s):  
Ana Marín Celestino ◽  
Diego Martínez Cruz ◽  
Elena Otazo Sánchez ◽  
Francisco Gavi Reyes ◽  
David Vásquez Soto
Keyword(s):  
Principal Component Analysis ◽  
Spatial Analysis ◽  
Groundwater Quality ◽  
Quality Assessment ◽  
Principal Component ◽  
Component Analysis ◽  
Groundwater Quality Assessment

Principal Component Analysis of Hydrological Data

2010 ◽  
pp. 364-388
Author(s):  
Petr Praus
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Ground Water ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Hydrological Data ◽  
First Case

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.

Sign in / Sign up

Export Citation Format

Share Document