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IJSTR >> Volume 7 - Issue 5, May 2018 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Mining Of Geophysical Data To Predict Groundwater Prospect In A Basement Complex Terrain Of Southwestern Nigeria

[Full Text]



Adiat, K.A.N., Osifila, A.J., Akinlalu, A. A., Alagbe, O.A.



electrical resistivity, schlumberger array, analytic hierarchy process, prediction model.



This study applied the multi criteria decision analysis to geophysical data acquired to achieve an unbiased integration of geoelectric parameters obtained from the results of the interpretation of the geophysical data in the order of their hydrogeologic importance to consequently produce a groundwater prediction model. The geoelectric parameters which were the coefficient of anisotropy, aquifer resistivity, aquifer thickness, overburden thickness and overburden resistivity were the main subsurface factors controlling the flow and accumulation of groundwater resources in the study area. Each parameter was assigned appropriate weight based on Saaty’s nine-point scale and the weights were normalized through the Analytic Hierarchy Process (AHP). The assigned weights were adjudged to be consistent and unbiased through the attainment of consistency ratio of less than 10%. The groundwater prediction model of the area was produced from the application of the Groundwater Potential Index (GWPI) model equation developed. The prediction model classified the groundwater potential of the study area into very low; very low – low; low – moderate; moderate - high and high - very high classes. The results obtained from this study established that the groundwater prediction model produced from the GWPI obtained from the application of multi criteria decision analysis to subsurface factors is accurate and reliable. It has also proven the ability of the methodology adopted as a good decision making tool in the presence of two or more conflicting criteria. This ability makes it a useful tool that can be adopted in the groundwater prediction domain.



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