Abstract:
The relationship between geoelectric parameters and groundwater column has not been fully
understood quantitatively. This is worrisome in the basement complex terrain where these
parameters are highly localized. In this study, an attempt was made to predict groundwater column
using Artificial Neural Network (ANN) technique that can offer a nonparametric approach in
modelling with higher reliability and precision. The ANN technique was applied to geoelectric
parameters derived from the results of the interpretation of fifty one Vertical Electrical Sounding
(VES) data acquired from the study area. The parameter values so obtained were input parameters
used for the training and validation of the ANN. These parameters were the overburden thickness,
aquifer thickness, overburden resistivity, aquifer resistivity and coefficient of anisotropy. The
results obtained were used to develop groundwater column prediction model. The model was
evaluated using two performance criteria namely; Mean Squared Error (MSE) and Regression
coefficient (R). The results of the validation showed less mean square error (MSE) of 0.0014286
and the high regression coefficient (R) of 0.98731. This indicates that ANN can be used to predict
groundwater column in a basement complex terrain with reasonably good accuracy. The ANN
model achieved high predictive accuracy.
