Groundwater Quality Variation and Regression Analysis � a Case Study Around Municipal Dumpsite in India

Abstract

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.