PREDICTION MODELS OF GROUNDWATER QUALITY PARAMETERS IN AN IRRIGATED AREA WITH WASTEWATER NEAR HYDERABAD, INDIA

According to International Groundwater Resources Assessment Centre (IGRAC), “Groundwater is vital resource that provides almost half of all drinking water worldwide, about forty percent of water required for irrigated agriculture and about one third of water required for industry”. The importance of groundwater is now internationally recognized as the theme of United Nations world water day 2022 is “Groundwater: Making the invisible visible”. This paper takes care of United Nations sustainable development goal number six of achieving clean water and sanitation with eight targets by 2030. Electrical Conductivity (EC), Total dissolved solids (TDS), Turbidity (TURB), Sodium (Na), Sulfate (SO4), Sodium Adsorption Ratio (SAR), Calcium (Ca), Magnesium (Mg), Biological oxygen demand (BOD), Dissolved Oxygen (DO), Chemical oxygen demand (COD), Potential of Hydrogen (pH) etc. are the most common groundwater quality parameters. The wastewater from Musi river is being used for agriculture irrigation in the study area. Long-term use of wastewater for irrigation causes soil pollution and groundwater contamination. The groundwater samples are collected from all nine bore wells in the study area every three months in the year 2021. Thus, there are thirty six values during the entire year for each of various groundwater quality parameters. Three multiple linear regression (MLR) models are developed using regression tool available in Microsoft Excel. F-tests ant t-tests are used to measure the goodness of fit of MLR equations developed. The MLR model predicting EC has the highest multiple correlation coefficient (multiple R) value of 0.999 and it is followed by MLR model predicting SAR with multiple R value of 0.995. The values of coefficient of multiple determination (R square) of three MLR models developed indicate that the explained variance is 0.998, 0.991 and 0.903 respectively. From this we can conclude that the unexplained variance is 0.002, 0.009 and 0.097 respectively. The Fobserved values are higher than the Fcritical values for single-tailed test obtained from the standard tables at 5% level of significance. This shows that all the independent variables are statistically related to the dependent variables used for prediction at 95% confidence level. t- statistic observed values of all the independent variables are higher than the t-critical values obtained from the standard tables for single-tailed test at 5% level of significance. This indicates that all independent variables used in three MLR equations are statistically significant and they are useful in the prediction of dependent variables at 5% level of significance.