scholarly journals Cluster analysis of water-quality data for Lake Sakakawea, Audubon Lake, and McClusky Canal, central North Dakota, 1990-2003

2006 ◽  
Author(s):  
Karen R. Ryberg
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
North Dakota ◽  
Quality Data ◽  
Water Quality Data

scholarly journals Application of multivariate statistical techniques for investigating climate change and anthropogenic effects on surface water quality assessment: case study of Zohreh river, Hendijan, Iran

2021 ◽  
Vol 11 (6) ◽  
Author(s):  
Jalal Valiallahi ◽  
Saideh Khaffaf Roudy
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Monitoring Program ◽  
T Test ◽  
Total Variance ◽  
Quality Data ◽  
Water Quality Data ◽  
Significant Difference ◽  
The Mean

AbstractIn the present study, evaluation of spatial variations and interpretation of Zohrehh River water quality data were made by using multivariate analytical techniques including factor analysis and cluster analysis also the Arc GIS® software was used. The research method was formulated to achieve objectives herein, including field observation, numerical modeling, and laboratory analyses. The results showed that dataset consisted of 11,250 observations of seven-year monitoring program (measurement of 15 variables at 3 main stations from April 2010 to March 2017). Factor analysis with principal component analysis extraction of the dataset yielded seven varactors contributing to 82% of total variance and evaluated the incidence of each varactor on the total variance. The results of cluster analysis became complete with t-test and made water quality comparison between two clusters possible. Results of factor analysis were employed to facilitate t-test analysis. The t-test revealed the significant difference in a confidence interval of 95% between the mean of calculated varactors 1, 2, 6 and 7 between two clusters, but there was no significant difference in the mean of other varactors 3, 4 and 5 between two groups. The result shows the effect of agricultural fertilizers on stations located at downstream of the ASK dam.

Water Quality Data Analysis Based on Cluster Algorithm

2014 ◽  
Vol 496-500 ◽  
pp. 1919-1922
Author(s):  
Jun Ou ◽  
Shu Qing Li
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Data Storage ◽  
Water Environment ◽  
Quality Data ◽  
Quality Level ◽  
Modeling Tools ◽  
Water Quality Data ◽  
Data Mining Algorithms ◽  
Collection Data

In this paper, it has introduced cluster analysis of data mining algorithms in detail. Hierarchical clustering and partitioning method are emphasized. The principles of mathematics are elaborated. The monitoring system of water environment is composed of data collection, data transmission, data storage and data reasoning components. Cluster analysis applies to the data storage behavior. With the analysis, the key elements determining the water quality level are modeled easily. The modeling tools has created good quality information module, defining classes and attributes. It has reduced the database storage, analysis workload and prepared for effective ontology analysis.

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