Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables

Abstract The precise estimation of spatial variation of groundwater quality variables is necessary for making appropriate decisions regarding aquifer resources management and environmental systems. The aim of this research is to evaluate five different data-intelligent methods so as to get the best one in spatial estimation of electrical conductivity (EC) and total hardness (TH) of groundwater resources. The study uses water quality data including Na, Cl, Mg, Ca, SO4, HCO3, TH, and EC, observed at 367 wells, in the Chhattisgarh state, central India. Four different scenarios are decided by implementing Gamma and linear regression methods. Data-intelligent methods applied to selected scenarios to estimate EC and TH are dynamic evolving neural-fuzzy inference system (DENFIS), group method of data handling (GMDH), multivariate adaptive regression spline (MARS), M5 Tree model (M5 Tree), and gene expression programming (GEP). Evaluation of the methods with respect to correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), Legates and McCabe index (LMI), and Willmott index of agreement (WI) indicates that the GEP for the input combination including TH, Na, Cl, HCO3 variables and the DENFIS by employing Ca, Mg, EC variables provide the best accuracy in estimating EC and TH, respectively. The statistical metrics of the GEP (DENFIS) for the best scenario are R = 0.970 (0.958), NSE = 0.941 (0.917), RMSE = 105.317 μs/cm (34.692 mg/L), MAE = 57.075 μs/cm (12.603 mg/L), WI = 0.984 (0.979) and LMI = 0.824 (0.868). The spatial distribution maps indicate that the EC and TH values of groundwater resources in the studied area are in their permissible limits for drinking consumption and the interpolated maps of the data-intelligent techniques have suitable precision in comparison with the observed maps.