Water Quality Assessment of an Unexplored Tropical Freshwater System in Thiruvananthapuram, India: A Multivariate Statistical Approach

2018 ◽  
pp. 1-24
Author(s):  
Anila P. Ajayan ◽  
Jan W. Rijstenbil ◽  
K. G. Ajit Kumar
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Statistical Approach ◽  
Water Quality Assessment ◽  
Multivariate Statistical ◽  
Multivariate Statistical Approach ◽  
Freshwater System ◽  
Tropical Freshwater

Surface Water Quality Assessment of Lis River Using Multivariate Statistical Methods

2012 ◽  
Vol 223 (9) ◽  
pp. 5549-5561 ◽  
Author(s):  
Judite S. Vieira ◽  
José C. M. Pires ◽  
Fernando G. Martins ◽  
Vítor J. P. Vilar ◽  
Rui A. R. Boaventura ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Assessment ◽  
Statistical Methods ◽  
Surface Water Quality ◽  
Water Quality Assessment ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Lis River

A GIS-Based Spatial-Multivariate Statistical Analysis of Water Quality Data in the Porsuk River, Turkey

2009 ◽  
Vol 44 (3) ◽  
pp. 279-293 ◽  
Author(s):  
Ozan Arslan
Keyword(s):  
Water Quality ◽  
Statistical Analysis ◽  
Quality Assessment ◽  
Statistical Methods ◽  
Multivariate Statistical Analysis ◽  
Water Quality Assessment ◽  
Quality Data ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Porsuk River

Abstract The study offers a GIS-based multivariate statistical analysis strategy to assess river water quality. Multivariate statistical methods and Geographic Information System (GIS) technology have effectively been used for water quality management. Recognizing the fact that the use of standard statistical methods can be restrictive due to the complexity of water quality datasets, geospatial statistical methods have been recommended for the water quality assessment. The objective of the study was to explore the potential capabilities of GIS-based joint multivariate statistical analysis for water quality assessment of Porsuk River in Turkey. A well-known multivariate statistical technique, principal component analysis (PCA), is incorporated into a geographic database for interpretation of water quality data. To characterize spatial variability of water quality data, spatial PCA was performed on the basis of spatial autocorrelation. Application of the joint spatio-multivariate statistical analysis for interpretation of the water quality database offered a better understanding of the hydrochemistry in the study region.

scholarly journals Water Quality Assessment of a Tropical Mexican Lake Using Multivariate Statistical Techniques

2015 ◽  
Vol 06 (03) ◽  
pp. 215-224 ◽  
Author(s):  
Jessica Badillo-Camacho ◽  
Eire Reynaga-Delgado ◽  
Isela Barcelo-Quintal ◽  
Pedro F. Zarate del Valle ◽  
Ulrico J. López-Chuken ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical
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