Abstract:
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, ARDBNN
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.
