FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2032047304> ?p ?o ?g. }

• W2032047304 endingPage "3992" @default.
• W2032047304 startingPage "3980" @default.
• W2032047304 abstract "This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994-1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources." @default.
• W2032047304 created "2016-06-24" @default.
• W2032047304 creator A5005872471 @default.
• W2032047304 creator A5017934209 @default.
• W2032047304 creator A5047651371 @default.
• W2032047304 creator A5048702806 @default.
• W2032047304 date "2004-11-01" @default.
• W2032047304 modified "2023-10-18" @default.
• W2032047304 title "Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—a case study" @default.
• W2032047304 cites W1524229245 @default.
• W2032047304 cites W1908961647 @default.
• W2032047304 cites W1975640350 @default.
• W2032047304 cites W1985493764 @default.
• W2032047304 cites W1993933742 @default.
• W2032047304 cites W2005644000 @default.
• W2032047304 cites W2008505918 @default.
• W2032047304 cites W2009097930 @default.
• W2032047304 cites W2033923757 @default.
• W2032047304 cites W2042733458 @default.
• W2032047304 cites W2068972657 @default.
• W2032047304 cites W2083274400 @default.
• W2032047304 cites W2138436021 @default.
• W2032047304 cites W2161860494 @default.
• W2032047304 cites W2164303307 @default.
• W2032047304 cites W2168062337 @default.
• W2032047304 cites W2172185496 @default.
• W2032047304 cites W2736764543 @default.
• W2032047304 cites W4211088641 @default.
• W2032047304 doi "https://doi.org/10.1016/j.watres.2004.06.011" @default.
• W2032047304 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15380988" @default.
• W2032047304 hasPublicationYear "2004" @default.
• W2032047304 type Work @default.
• W2032047304 sameAs 2032047304 @default.
• W2032047304 citedByCount "1298" @default.
• W2032047304 countsByYear W20320473042012 @default.
• W2032047304 countsByYear W20320473042013 @default.
• W2032047304 countsByYear W20320473042014 @default.
• W2032047304 countsByYear W20320473042015 @default.
• W2032047304 countsByYear W20320473042016 @default.
• W2032047304 countsByYear W20320473042017 @default.
• W2032047304 countsByYear W20320473042018 @default.
• W2032047304 countsByYear W20320473042019 @default.
• W2032047304 countsByYear W20320473042020 @default.
• W2032047304 countsByYear W20320473042021 @default.
• W2032047304 countsByYear W20320473042022 @default.
• W2032047304 countsByYear W20320473042023 @default.
• W2032047304 crossrefType "journal-article" @default.
• W2032047304 hasAuthorship W2032047304A5005872471 @default.
• W2032047304 hasAuthorship W2032047304A5017934209 @default.
• W2032047304 hasAuthorship W2032047304A5047651371 @default.
• W2032047304 hasAuthorship W2032047304A5048702806 @default.
• W2032047304 hasConcept C105795698 @default.
• W2032047304 hasConcept C127413603 @default.
• W2032047304 hasConcept C161584116 @default.
• W2032047304 hasConcept C16828302 @default.
• W2032047304 hasConcept C178790620 @default.
• W2032047304 hasConcept C185592680 @default.
• W2032047304 hasConcept C187320778 @default.
• W2032047304 hasConcept C18903297 @default.
• W2032047304 hasConcept C205649164 @default.
• W2032047304 hasConcept C27438332 @default.
• W2032047304 hasConcept C2780797713 @default.
• W2032047304 hasConcept C33923547 @default.
• W2032047304 hasConcept C38180746 @default.
• W2032047304 hasConcept C39432304 @default.
• W2032047304 hasConcept C45812177 @default.
• W2032047304 hasConcept C58640448 @default.
• W2032047304 hasConcept C69738355 @default.
• W2032047304 hasConcept C76886044 @default.
• W2032047304 hasConcept C86803240 @default.
• W2032047304 hasConcept C94747663 @default.
• W2032047304 hasConceptScore W2032047304C105795698 @default.
• W2032047304 hasConceptScore W2032047304C127413603 @default.
• W2032047304 hasConceptScore W2032047304C161584116 @default.
• W2032047304 hasConceptScore W2032047304C16828302 @default.
• W2032047304 hasConceptScore W2032047304C178790620 @default.
• W2032047304 hasConceptScore W2032047304C185592680 @default.
• W2032047304 hasConceptScore W2032047304C187320778 @default.
• W2032047304 hasConceptScore W2032047304C18903297 @default.
• W2032047304 hasConceptScore W2032047304C205649164 @default.
• W2032047304 hasConceptScore W2032047304C27438332 @default.
• W2032047304 hasConceptScore W2032047304C2780797713 @default.
• W2032047304 hasConceptScore W2032047304C33923547 @default.
• W2032047304 hasConceptScore W2032047304C38180746 @default.
• W2032047304 hasConceptScore W2032047304C39432304 @default.
• W2032047304 hasConceptScore W2032047304C45812177 @default.
• W2032047304 hasConceptScore W2032047304C58640448 @default.
• W2032047304 hasConceptScore W2032047304C69738355 @default.
• W2032047304 hasConceptScore W2032047304C76886044 @default.
• W2032047304 hasConceptScore W2032047304C86803240 @default.
• W2032047304 hasConceptScore W2032047304C94747663 @default.
• W2032047304 hasIssue "18" @default.
• W2032047304 hasLocation W20320473041 @default.
• W2032047304 hasLocation W20320473042 @default.
• W2032047304 hasOpenAccess W2032047304 @default.
• W2032047304 hasPrimaryLocation W20320473041 @default.
• W2032047304 hasRelatedWork W1435548451 @default.
• W2032047304 hasRelatedWork W2032047304 @default.

• next