Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis

Appropriate assessment of groundwater is important to ensure sustainable and safe use of this natural resource. However, evaluating overall groundwater quality is difficult due to the spatial variability of multiple contaminants. This research proposes a geographical information system (GIS)-based groundwater quality pollution mapping technique, which synthesizes different available water quality data, normalized with the World Health Organization (WHO) standards. The normalized difference index (NDI) is used to perform the normalization process. This study utilizes a multi-criteria evaluation (MCE) script (MATLAB 10.0), developed to assign weights to each of the analysed water quality parameters. The consistency of judgments of weight assignment is further analysed using the consistency ratio (CR) and consistency index (CI) techniques. The Shuttle Radar Topography Mission (SRTM) C-band radar and Landsat TM satellite image data are used to derive a digital elevation model (DEM) and land-use/land-cover map of the area. A new sensitivity analysis method is introduced to estimate the responsible factors associated with the proposed groundwater pollution zone model (GPZM). Multivariate analysis methods, such as factor analysis (FA), cluster analysis (CA) and principal component analysis (PCA), are used to uncover the latent structure of the data, to understand the correlations across hierarchical levels, and for dimensionality reduction, respectively.