## Assessment of Groundwater Quality Using GIS - A Case Study of the Churu District of Rajasthan - page 01

Noha Donia (2011), used the IDW for interpolating the samples values and uses equations: qi=100(vi/si), wqi=sigma (i=1 to n) (qi) and AWQI= sigma (qi)(i=1 to n)/n to calculate the index. The value between 0 to 100 is considered as good quality and above is not recommended for drinking use. Ramakrishnaiah et al. (2009) and Ishaku (2011) have used the relative weight method to calculate the index value, i.e. a weight is assigned to each parameter is divided by the sum of all weights. A quality rating scale (Q Where SIi is the sub index of ith parameter; Q Another set of equations used to calculate the WQI is given from equation B.1 to Eq B.3 (Tiwari and Manzoor, 1988). The quality rating q Where V |
The overall water quality index was calculated by aggregating these quality ratings linearly as follows, Where n = number of parameters. The average water quality index (AWQI) for n parameters was calculated using this equation, Katyal (2011) et al studied many quality indexes for water and suggested that every area has been affected by different parameters and that different weights could be used in various regions. F Ghadimi (2012) has used 2 indices and done statistical analysis of them to calculate the WQI of water. He also concluded that hydrochemical data were classified in to 2 main groups: 1 - natural and; 2 - mining or leaching sources. Mouna (2011) used the simple WQI equation to calculate the qi for water using the weightage for various parameters analysed for the samples. They categorised each parameter into 3 categories i.e. permissible limit, below limit and above limit and produced the spatial distribution map of all parameters. From the literature it is clear that all have attempted a small area for their study with a maximum of 25 samples for calculating the WQI. It would be easier to handle complex equations with a smaller number of samples in calculating WQI but when a large area and huge number of samples will be used, then simple equations would be used to maintain control on measurement of WQI. Cluster analysis (CA) is a simple approach for classification of groundwater quality into two or more mutually exclusive unknown groups based on the combination of interval variables (Hussein, 2004). The tool sorts out different objects into groups such that the degree of association between the objects is maximal if they belong to the same group (Hamzaoui-Azaza et al., 2009). The hierarchal cluster analysis according to Ward (1963), with squared Euclidean distances, was applied to detect multivariate similarities in groundwater quality. |