Assessment Of Water Quality Using Multi - Variate Analysis,Water Quality Index And Geo - Statistical Analysis
Journal Title: International Journal of Scientific Research and Management - Year 2015, Vol 3, Issue 8
Abstract
The water quality is mostly characterized by many variables (parameters) which represent a water composition in specific local i ties and time. Real hydrological data are mostly noisy, it means that they are not normally distribu t ed, ofte n co - linear or auto correlated, containing outliers or errors etc. These data sets create a n - dimensional space from which information about the w a ter composition has to be mined. For this purpose, multivariate methods such as the Principal Component Ana lysis(PCA), the Fa c tor Analysis, and the Discriminate Analysis, are used. An alternative is not to use all the quality pa- rameters themselves, but instead of them the indexes that synthesize the typical similarities and differences between the major ions i n the surface water environment. These new variables (indexes) can be easily identified by means of a factorial method. Then trends can be an a lyzed by a robust tec h nique. In the present investigation, an attempt has been made to study Multi - variate stati stical analysis of groun d water collected from different sampling points during different seasons. This project objective is to assess existing water quality condition and future water quality conditions for selected parame- ters. The basic aim is to use PCA for reducing the number of parameters without losing much information which was de- termined during the three season and to recognize basic feature of water quality. water quality index is one of the most ef- fective tool to communicate the i n formation on the quality of any water body. Geo - statistical technique, namel y O rdinary K riging is been used to e n hance th e water quality data prediction . This is widely and the most prominent spatial statistical analysis method in Estimation
Authors and Affiliations
Miss. Kavya. S
Keywords
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