With respect to groundwater deterioration from human activities a unique situation of co-disposal of non-engineered Municipal Solid Waste (MSW) dumping and Secondary Wastewater (SWW) disposal on land prevails simultaneously within the same campus at Puducherry in India. Broadly the objective of the study is to apply and compare Artificial Neural Network (ANN) and Multi Linear Regression (MLR) models on groundwater quality applying Canadian Water Quality Index (CWQI). Totally, 1065 water samples from 68 bore wells were collected for two years on monthly basis and tested for 17 physio-chemical and bacteriological parameters. However the study was restricted to the pollution aspects of 10 physio-chemical parameters such as EC, TDS, TH,, Mg 2+ and K + . As there is wide spatial variation (2 to 3 km radius) with ground elevation (more than 45 m) among the bore wells it is appropriate to study the groundwater quality using Multivariate Statistical Analysis and ANN. The selected ten parameters were subjected to Hierarchical Cluster Analysis (HCA) and the clustering procedure generated three well defined clusters. Cluster wise important physiochemical attributes which were altered by MSW and SWW operations, are statistically assessed. The CWQI was evolved with the objective to deliver a mechanism for interpreting the water quality data for all three clusters. The ANOVA test results viz., F-statistic (F = 134.55) and p-value (p = 0.000 < 0.05) showed that there are significant changes in the average values of CWQI among the three clusters, thereby confirming the formation of clusters due to anthropogenic activities. The CWQI simulation was performed using MLR and ANN models for all three clusters. Totally, 1 MLR and 9 ANN models were considered for simulation. Further the performances of ten models were compared using R 2 , RMSE and MAE (quantitative indicators). The analyses of the results revealed that both MLR and ANN models were fairly good in pre- 100dicting the CWQI in Clusters 1 and 2 with high R 2 , low RMSE and MAE values but in Cluster 3 only ANN model fared well. Thus this study will be very useful to decision makers in solving water quality problems.

At Lawspet area in Puducherry, India, a unique situation of co-disposal of solid waste dumping and secondary wastewater disposal on land, prevails simultaneously within the same campus. So an attempt is made to assess the combined effect of this co-disposal on the environmental quality and pollution effects on groundwater quality with a view to correctly monitor the situation. Multivariate statistical analysis like hierarchical cluster analysis (HCA) and discriminant analysis (DA) were employed. HCA was performed on borewells, physiochemical parameters and seasons. Borewell clustering identified four clusters illustrating varying degree of groundwater contamination. In parameter clustering, two major clusters were formed indicating hardness and anthropogenic components. Temporal clustering identified three major clusters indicating pre-monsoon, monsoon and post-monsoon. Discriminant analysis revealed nine significant parameters which discriminate four clusters qualitatively affording 86% correct assignation to discriminate among the clusters. Also three major components viz. anthropogenic, hardness and geogenic responsible for groundwater quality in the study area were identified. Conclusively the investigation revealed that the direction of the contaminant transport is towards the southeast direction of the study area, where all the borewells (100%) are affected.

In ground water quality studies multivariate statistical techniques like Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Factor Analysis (FA) and Multivariate Analysis of Variance (MANOVA) were employed to evaluate the principal factors and mechanisms governing the spatial variations and to assess source apportionment at Lawspet area in Puducherry, India. PCA/FA has made the first known factor which showed the anthropogenic impact on ground water quality and this dominant factor explained 82.79% of the total variance. The other four factors identified geogenic and hardness components. The distribution of first factor scores portray high loading for EC, TDS, Na + and Cl − (anthropogenic) in south east and south west parts of the study area, whereas other factor scores depict high loading for direction. Further MANOVA showed that there are significant differences between ground water quality parameters. The spatial distribution maps of water quality parameters have rendered a powerful and practical visual tool for defining, interpreting, and distinguishing the anthropogenic, hardness and geogenic factors in the study area. Further the study indicated that multivariate statistical methods have successfully assessed the ground water qualitatively and spatially with a more effective step towards ground water quality management.