GROUNDWATER POTENTIAL MODELING USING INTEGRATED REMOTE SENSING AND GEOPHYSICAL METHODS IN A TYPICAL BASEMENT COMPLEX

Abstract

Delineation of geologic features that are capable of hosting water in economic quantity in the Basement Complex has been a major concern because they are usually localized due to restricted fractured and weathered rock. An approach engaging Vertical Electrical Sounding (VES) and remotely sensed data was carried out with a view to producing groundwater potential model of Oke-Ilewo Community, Abeokuta, Nigeria. Sixty two (62) depth sounding data were acquired using Schlumberger array with (AB/2) ranging from 1– 120 m. The VES were quantitatively interpreted using autopartial curve match software and computer aided iteration to determine the geo-electrical parameters. Twelve parameters of hydrogeological importance were used to develop groundwater potential model for the study area. This was subjected to the Gradient Boosting Tree (GBT) model using the Salford Predictive Modeler 8.0 software. Four (4) were from remotely sensed data. The other eight (8) of these parameters were derived from the interpreted results of geophysical data. The data were partitioned into Training and test dataset in ratio 90:10 using k 10 cross validation techniques. Their prediction importance was determined and the groundwater potential index calculated. The groundwater potential map (GPM) of the area revealed three (3) zonations, which are the low groundwater potential, the moderate groundwater and the high groundwater potential with percentage frequency of 46%, 10%, and 44%. Water column of wells in the area was used to validate the GPM developed. This was done by superimposing the values in the GPM and observed the accuracy. The model gave an accuracy of 87%. In conclusion, the model has proven that the drop in aquifer resistivity doesn't necessitate the presence of groundwater but rather several parameter should be integrated together to better understand the true nature of the aquifer.