Hydrogeochemical characterization of groundwater of peninsular Indian region using multivariate statistical techniques

Assessment of spatial changes of stream erosion under environmental factors in Kojoor Firoozkolay region using multivariate statistical techniques

climatic regionalization of caspian region using multivariate statistical methods

the weaknesses of the traditional climatic classification methods in presenting the climatic realities of caspian region and showing the new methods' merits based on multivariate statistical methods, and geographical information system in presenting the climatic regions of caspian, are among the most important ideas of conducting this study. to do so, we used the data of synoptic stations ...

Hydrogeochemical Evolution of Groundwater Resource in an Arid Region of Southeast Iran (Ravar plain–Kerman province)

This study was carried out in the Ravar plain, a typical arid zone in southeastern Iran, with the objectives of evaluating hydrochemical quality of the groundwater resources and identifying the processes that modify the groundwater composition. Groundwater samples were collected from representative wells spread over the study area. Major cations and anions along with physico-chemical parameters...

Statistical Analysis of the Hydrogeochemical Evolution of Groundwater in Alluvial Aquifer of Arak Mighan Playa, Markazi Province, Iran

This paper presents results of hydro-chemical processes controlling groundwater chemical composition, using an integrated application of hierarchical cluster analysis and factor analysis of a major ion data set of groundwater from Mighan playa aquifer. Cluster analysis classified samples into four clusters(A, B, C and D) according to their dominant chemical composition: cluster A (dominant comp...

Evaluation of seasonal variability in surface water quality of Shallow Valley Lake, Kashmir, India, using multivariate statistical techniques

Seasonal variation in water quality of Anchar Lake was evaluated using multivariate statistical techniques- principal component analysis (PCA) and cluster analysis (CA). Water quality data collected during 4 seasons was analyzed for 13 parameters. ANOVA showed significant variation in pH (F3 = 10.86, P < 0.05), temperature (F3 = 65, P