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Bibliographic Data
Control Number310108
Date and Time of Latest Transaction20150706093533.AM
General Information150706s |||||||||b ||00|||
Cataloging SourceSTII-DOST
Local Call NumberScienceDirect
Main Entry - Personal NameTorkian, Ayoob
 Abrishamchi, Ahmad
 Razmkhah, Homa
Title StatementEvaluation of spatial and temporal variation in water quality by pattern recognition techniques A case study on Jajrood River (Tehran, Iran) by Homa Razmkhah, Ahmad Abrishamchi and Ayoob Torkian
Physical Descriptionpages 852-860 computer file; text; 447kb
Summary, Etc.In this paper, principal component analysis (PCA) and hierarchical cluster analysis (CA) methods have been used to investigate the water quality of Jajrood River (Iran) and to assess and discriminate the relative magnitude of anthropogenic and "natural" influences on the quality of river water. T, EC, pH, TDS, NH(4), NO(3), NO(2), Turb., T.Hard., Ca, Mg, Na, K, Cl, SO(4), SiO(2) as physicochemical and TC, FC as biochemical variables have been analyzed in the water samples collected every month over a three-year period from 18 sampling stations along a 50 km section of Jajrood River that is under the influence of anthropogenic and natural changes. Exploratory analysis of experimental data has been carried out by means of PCA and CA in an attempt to discriminate sources of variation in water quality. PCA has allowed identification of a reduced number of mean 5 varifactors, pointing out 85% of both temporal and spatial changes. CA classified similar water quality stations and indicated Out-Meygoon as the most polluted one. Ahar, Baghgol, Rooteh, Befor Zaygan, Fasham, Roodak and Lashgarak were identified as affected by organic pollution. A Scree plot of stations in the first and second extracted components on PCA also gave us a classification of stations due to the similarity of pollution sources. CA and PCA led to similar results, though Out-Meygoon was identified as the most polluted station in both methods. Box-plots showed that PCA could approximately demonstrate temporal and spatial variations. CA gave us an overview of the problem and helped us to classify and better explain the PCA results
Subject Added Entry - Topical TermEngineering
 Principal component analysis
 Exploratory data analysis
 Cluster analysis
LocationDOST STII ScienceDirect NONPRINTS NP 13-14927 1 13-14927 Online/Download 2010-12-02
  
     
 
Physical Location
Department of Science and Technology
Science and Technology Information InstituteScienceDirect
  
     
 
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