Estimation of spatial variability of aquifer parameters from geophysical methods: a case study of Sindhudurg district, Maharashtra, India

• Published in: Stochastic environmental research and risk assessment 2017-09, Vol.31 (7), p.1709-1726
• Main Authors: Das, Anasuya, Maiti, Saumen, Naidu, Suneetha, Gupta, Gautam
• Format: Article
• Language: English
• Subjects: Aquatic Pollution; Aquifer systems; Aquifers; Artificial neural networks; Bayesian analysis; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Data processing; Earth and Environmental Science; Earth Sciences; Electrical conductivity; Electrical resistivity; Environment; Fuzzy logic; Geochemistry; Geophysical methods; Geophysics; Groundwater; Groundwater management; Hydraulics; Kriging interpolation; Math. Appl. in Environmental Science; Mathematical models; Neural networks; Original Paper; Parameter estimation; Physics; Porosity; Probability Theory and Stochastic Processes; Pumping; Pumping tests; Resource management; Statistics for Engineering; Transmissivity; Waste Water Technology; Water Management; Water Pollution Control; Water resources; Water wells
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-016-1317-4

Accurate estimation of aquifer parameters, especially from crystalline hard rock area, assumes a special significance for management of groundwater resources. The aquifer parameters are usually estimated through pumping tests carried out on water wells. While it may be costly and time consuming for carrying out pumping tests at a number of sites, the application of geophysical methods in combination with hydro-geochemical information proves to be potential and cost effective to estimate aquifer parameters. Here a method to estimate aquifer parameters such as hydraulic conductivity, formation factor, porosity and transmissivity is presented by utilizing electrical conductivity values analysed via hydro-geochemical analysis of existing wells and the respective vertical electrical sounding (VES) points of Sindhudurg district, western Maharashtra, India. Further, prior to interpolating the distribution of aquifer parameters of the study area, variogram modelling was carried out using data driven techniques of kriging, automatic relevance determination based Bayesian neural networks (ARD-BNN) and adaptive neuro-fuzzy neural networks (ANFIS). In total, four variogram model fitting techniques such as spherical, exponential, ARD-BNN and ANFIS were compared. According to the obtained results, the spherical variogram model in interpolating transmissivity, ARD-BNN variogram model in interpolating porosity, exponential variogram model in interpolating aquifer thickness and ANFIS variogram model in interpolating hydraulic conductivity outperformed rest of the variogram models. Accordingly, the accurate aquifer parameters maps of the study area were produced by using the best variogram model. The present results suggest that there are relatively high value of hydraulic conductivity, porosity and transmissivity at Parule, Mogarne, Kudal, and Zarap, which would be useful to characterize the aquifer system over western Maharashtra.