Stochastic models applied to cluster analysis of censored water quality data

1993 ◽  
Vol 29 (8) ◽  
pp. 2705-2711 ◽  
Author(s):  
A. Maul ◽  
A. H. El-Shaarawi
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Stochastic Models ◽  
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.

scholarly journals Interpretation of Water Quality Data in uMngeni Basin (South Africa) Using Multivariate Techniques

2021 ◽  
Author(s):  
Innocent Rangeti ◽  
Bloodless Dzwairo
Keyword(s):  
South Africa ◽  
Water Quality ◽  
Cluster Analysis ◽  
Quality Data ◽  
Multivariate Techniques ◽  
Data Sets ◽  
Water Sampling ◽  
Water Quality Data ◽  
Cluster A ◽  
Spatio Temporal

The major challenge with regular water quality monitoring programmes is making sense of the large and complex physico-chemical data-sets that are generated in a comparatively short period of time. Consequentially, this presents difficulties for water management practitioners who are expected to make informed decisions based on information extracted from the large data-sets. In addition, the nonlinear nature of water quality data-sets often makes it difficult to interpret the spatio-temporal variations. These reasons necessitated the need for effective methods of interpreting water quality results and drawing meaningful conclusions. Hence, this study applied multivariate techniques, namely Cluster Analysis and Principal Component Analysis, to interpret eight-year (2005–2012) water quality data that was generated from a monitoring exercise at six stations in uMngeni Basin, South Africa. The principal components extracted with eigenvalues of greater than 1 were interpreted while considering the pollution issues in the basin. These extracted components explain 67–76% of the water quality variation among the stations. The derived significant parameters suggest that uMngeni Basin was mainly affected by the catchment’s geological processes, surface runoff, domestic sewage effluent, seasonal variation and agricultural waste. Cluster Analysis grouped the sampling six stations into two clusters namely heavy (B) or low (A), based on the degree of pollution. Cluster A mainly consists of water sampling stations that were located in the outflow of the dam (NDO, IDO, MDO and NDI) and its water can be described as of fairly good quality due to dam retention and attenuation effects. Cluster B mainly consist of dam inflow water sampling stations (MDI and IDI), which can be described as polluted if compared to cluster A. The poor quality water observed at Cluster B sampling stations could be attributed to natural and anthropogenic activities through point source and runoff. The findings could assist in determining an appropriate set of water quality parameters that would indicate variation of water quality in the basin, with minimum loss of information. It is, therefore, recommended that this approach be used to assist decision-makers regarding strategies for minimising catchment pollution.

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