Application of Surface Water Quality Classification Models Using Principal Components Analysis and Cluster Analysis

2019 ◽  
Author(s):  
Mohamed Ahmed Reda Hamed
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Surface Water ◽  
Principal Components ◽  
Surface Water Quality ◽  
Classification Models ◽  
Quality Classification ◽  
Water Quality Classification ◽  
Components Analysis ◽  
And Cluster Analysis

Surface water quality classification using data mining approaches: Irrigation along the Aladag River*

2021 ◽  
Author(s):  
Mohammad Taghi Sattari ◽  
Hajar Feizi ◽  
Muslume Sevba Colak ◽  
Ahmet Ozturk ◽  
Fazli Ozturk ◽  
...  
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Classification ◽  
Water Quality Classification ◽  
Using Data

scholarly journals Assessment of Water Quality in the Lakes Along Colentina River

2015 ◽  
Vol 13 ◽  
pp. 194-199
Author(s):  
Petra Ionescu ◽  
Violeta Monica Radu ◽  
Elena Diacu ◽  
Ecaterina Marcu
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Surface Water ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Ammonium Nitrogen ◽  
Entry And Exit ◽  
Quality Classification ◽  
Water Quality Classification ◽  
Calcium Magnesium

The purpose of this study is to evaluate the water quality in the lakes along Colentina River according to Romanian regulations referring to the norms on surface water quality classification, MO 161/2006. To achieve this goal, two sampling sections (entry and exit points) for each lake have been established, and the following indicators have been determined: pH, water temperature, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, nitrites, nitrates and ammonium nitrogen, total nitrogen, orthophosphates, total phosphorus, electrical conductivity, filterable residue, chlorides, sulphates, calcium, magnesium and sodium. Following this study, the variation of the concentrations of determined indicators in the two sampling sections for each lake has been assessed, as well as the classification into quality classes according to the before mentioned order.

Ranking e valutazione: il caso delle classifiche delle universitŕ

2009 ◽  
pp. 81-114
Author(s):  
Ferruccio Biolcati Rinaldi ◽  
Daniele Checchi ◽  
Chiara Guglielmetti ◽  
Silvia Salini ◽  
Matteo Turri
Keyword(s):  
Cluster Analysis ◽  
Key Words ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Partial Replication ◽  
Validity And Reliability ◽  
University Ranking ◽  
International Ranking ◽  
Components Analysis ◽  
And Cluster Analysis

- Abstract The paper consists of two parts. The first is more general: it introduces to university ranking, shows the leading international ranking, discusses the uses people make of rankings. The second focuses on Italian ranking Censis-la Repubblica developing two different kinds of analyses: after considering indicators validity and reliability, principal components analysis and cluster analysis are applied to a partial replication of Censis-la Repubblica data. A list of points to pay attention comes out of these analyses: it can be useful when defining rankings of complex institutions such as universities.Key words: ranking, university ranking, Censis-la Repubblica, validity and reliability, normalisation and combination of indicators.

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