Groundwater potentiality mapping using geoelectrical-based aquifer hydraulic parameters: A GIS-based multi-criteria decision analysis modeling approach

• Published in: TAO : Terrestrial, atmospheric, and oceanic sciences atmospheric, and oceanic sciences, 2017-06, Vol.28 (3), p.479-500
• Main Authors: Mogaji, Kehinde Anthony, Lim, Hwee San
• Format: Article
• Language: English
• ISSN: 1017-0839; 2311-7680
• DOI: 10.3319/TAO.2016.11.01.02

This study conducted a robust analysis on acquired 2D resistivity imaging data and borehole pumping test records to optimize groundwater potentiality mapping in Perak province, Malaysia using derived aquifer hydraulic properties. The transverse resistance (TR) parameter was determined from the interpreted 2D resistivity imaging data by applying the Dar-Zarrouk parameter equation. Linear regression and GIS techniques were used to regress the estimated values for TR parameters with the aquifer transmissivity values extracted from the geospatially produced BPT recordsbased aquifer transmissivity map to develop the aquifer transmissivity parameter predictive (ATPP) model. The reliability evaluated ATPP model using the Theil inequality coefficient measurement approach was used to establish geoelectrical-based hydraulic parameters (GHP) modeling equations for the modeling of transmissivity (Tr), hydraulic conductivity (K), storativity (St), and hydraulic diffusivity (D) properties. The applied GHP modeling equation results to the delineated aquifer media was used to produce aquifer potential conditioning factor maps for Tr, K, St, and D. The maps were modeled to develop an aquifer potential mapping index (APMI) model via applying the multi-criteria decision analysis-analytic hierarchy process principle. The area groundwater reservoir productivity potential model map produced based on the processed APMI model estimates in the GIS environment was found to be 71% accurate. This study establishes a good alternative approach to determine aquifer hydraulic parameters even in areas where pumping test information is unavailable using a cost effective geophysical data. The produced map can be explored for hydrological decision making.