Mixing data for multivariate statistical study of groundwater quality.

In the present paper, a multivariate statistical modeling study of water quality data from different places of Kozhikode Gity, Kerala, India, has been conducted applying multiple linear regression (MLR), structural equation modeling (SEM), and adaptive neuro-fuzzy inference system (ANFIS) modeling. First, we combined water quality data from different places in the study area over different time periods to obtain a unified multiple linear regression (MLR) model. By mixing three data sets from different places and time periods in four different ways, different regression models were formed with total dissolved solids (TDS) as the dependent variable and calcium, magnesium, nitrate, sodium, chloride, potassium, total hardness, and sulfate as independent variables.