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Groundwater Potential of Oke-Ana area southwestern Nigeria have been evaluated using the integration of electrical resistivity method, remote sensing and geographic information systems. The effect of five hydrogeological indices, namely lineament density, drainage density, lithology, overburden thickness and aquifer layer resistivity on groundwater occurrence was established. Multi-criteria decision analysis technique was employed to assign weight to each of the index using the concept of analytical hierarchy process. The assigned weight was normalized and consistency ratio was established. In order to evaluate the groundwater potential of Oke-Ana, sixty-seven (67) vertical electrical sounding points were occupied. Ten curve types were delineated in the study area. The curve types vary from simple three layer A and Htype curves to the more complex four, five and six layer AA, HA, KH, QH, AKH, HKH, KHA and KHKH curves. Four subsurface geo-electric sequences of top soil, weathered layer, partially weathered/fractured basement and the fresh basement were delineated in the area. The analytical process assisted in classifying Oke-Ana into, low, medium and high groundwater potential zones. Validation of the model from well information and two aborted boreholes suggest 70% agreement.

In order to investigate the competence of the proposed road for pavement stability, geotechnical and geophysical investigations involving Land Magnetic, Very Low Frequency Electromagnetic (VLF-EM) and Electrical Resistivity methods were carried out along Akure-Ipinsa road Southwestern Nigeria. The magnetic profile was qualitatively and quantitatively interpreted to produce geomagnetic section that provides information on the basement topography and structural disposition beneath the proposed road. Similarly, the VLF-EM profile was equally interpreted to provide information on the possible occurrence of linear features beneath the study area. These linear features pose a potential risk to the proposed road as they are capable of undermining the stability of the pavement structure. The geoelectric parameters obtained from the quantitative interpretation of the VES data were used to generate geoelectric section. The geoelectric section generated shows that the study area was underlain by four geoelectric layers namely the topsoil, the weathered layer, the partly weathered/fractured basement and the fresh basement. The major part of the topsoil, which constitutes the subgrade, is characterized by relatively low resistivity values (<100 Xm) suggestive of weak zones that are capable of undermining the stability of the proposed road. This therefore suggests that the layer is composed of incompetent materials that are unsuitable for engineering structures. Furthermore, fractured basement was also delineated beneath some portion of the proposed road. Since fracture is a weak zone, its presence can facilitate failure of the proposed road especially when it is occurring at shallow depth. The geotechnical results reveal that most of the investigated soil samples are clayey in nature. Integration of the results demonstrates that there is a good correlation between geophysical results and the geotechnical results. Furthermore, a vulnerability section that divided the road segments into three zones based on the degree of vulnerability was produced. These zones were high, moderate and low vulnerability zones. It is estimated that about 60% of the road segments constitutes moderate degree of vulnerability while 30% and 10% of the segments respectively constitute high and low degree of vulnerability.

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Empirical relationship between geoelectric parameters and groundwater level in boreholes/wells has not been established. Also, prediction of groundwater level from geoelectric parameters had hitherto not been reported. In order to overcome these challenges, the capability of artificial neural network (ANN) to model nonlinear system was explored in this study to predict groundwater level from geoelectric parameters. To achieve the above objectives, the ground water level (GWL) of all the accessible wells in the study area was obtained and this was used as the output parameter for the ANN model. A total of fifty-one (51) parametric vertical electrical soundings (VES) stations were occupied at each of the well location by adopting Schlumberger array configuration with electrode spacing (AB/2) ranging from 1 to 100 m. The VES data were quantitatively interpreted to generate geoelectric parameters believed to be controlling the groundwater flow and storage in the area. These parameters served as input for ANN model. The capability of ANN as a nonlinear modeling system was thereafter applied to produce a model that can predict the GWL from the input parameters. The efficiency of the model was evaluated by estimating the mean square error (MSE) and the regression coefficient (R) for the model. The results established that seasonal variation has little effect on the water fluctuation in the wells. Two aquifer types, weathered and fractured basement aquifer types, were delineated in the area. The results of the ANN model validation showed low MSE of 0.0014286 and the high regression coefficient (R) of 0.98731. This indicates that ANN can be used to predict GWL in a basement complex terrain with reasonably good accuracy. It is concluded that the ANN can effectively predict GWL from geoelectric parameters.