Water is an important resource for domestic, agriculture, and industrial purpose. The urban population requires a wide range of urban services including water supply, sewerage, and solid waste management. In most cities, the solid waste is dumped in open dumps without proper lining which affects the environmental media such as water and land. So the present study was focused on the impact of leachate on water quality. Water samples were collected from the Ramayanpatti dump site and the surrounding area. The water samples were tested for various physiochemical parameters and also predict the various effective irrigation indices such as Sodium Absorption Ratio, Residual Sodium Carbonate, Soluble Sodium Percent, Magnesium Absorption Ratio and Chloro-Alkaline Indices. Based on the Water Quality Index and irrigation indices most of the samples were not suitable for domestic as well as irrigation purposes. This indicates that the water is contaminated by leachate.

The quality of water around a municipal dumpsite is greatly affected by the leaching chemicals from the landfill. The aim of this study is to assess the groundwater quality and to develop and compare the performance of Statistical Package of Social Science (SPSS) regression and Artificial Neural Network models around municipal dumpsite in Tamil Nadu, India. The groundwater samples were collected every month from the 16 sampling points during the study period from January 2013 to December 2017. The physico chemical parameters of the samples such as pH, acidity, alkalinity, Hardness, Chloride, Sulphate and Total Dissolved Solids (TDS) were analysed and Water Quality Index (WQI) was arrived. From this data, the highest and the lowest polluted points S14 and S5 respectively, among the 16 sampling points was found. Correlation analysis showed that TDS exhibited a high positive correlation with chloride and hardness. Two models using SPSS regression and one model using ANN modeling were developed to predict the TDS in the sampling points. The prediction capabilities of the ANN were compared with the SPSS regression models. The maximum percentage of error obtained from ANN and SPSS were 7.5% and 15.6% at S5 sampling point. ANN models were more accurate than the SPSS multi nonlinear regression models having the same inputs and output.