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

Abstract

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.

Groundwater Aquifer Suitability for Irrigation Purposes Using Multi-Criteria Decision Approach in Salah Al-Din Governorate/Iraq

“…Therefore, GIS-based MCDA technique that efficiently combines multiple hydrological data to produce a reliable decision model is an effective tool to enhance the suitability analysis in an area of interest. Several researchers have used remote sensing and GIS-based MCDA in groundwater studies with effective results [21][22][23][24][25].…”

Modeling of geoelectric parameters for assessing groundwater potentiality in a multifaceted geologic terrain, Ipinsa Southwest, Nigeria – A GIS-based GODT approach

“…However, the uniqueness of these MCDA techniques' philosophy is such that their mechanisms allow systemic analysis and integrating of relevant criteria/factors to model or predicting target proposition that are traceable to mineralization potential mapping, groundwater potentiality mapping, vulnerability mapping etc (Elmahdy and Mohamed, 2014;Corsini et al, 2009;Carranza et al, 2008;Mogaji, 2017). Among the mostly used MCDA methods in groundwater prospectivity mapping is the analytical hierarchic processes (AHP) approach (Adiat et al, 2012;Chowdhury et al, 2009;Jha et al, 2010;Mogaji and Lim, 2016). According to these studies, many factors believed to be influencing or controlling groundwater potential i.e.…”

“…groundwater potentiality conditioning factors (GPCFs) often served as input indices synthesized for groundwater potentiality zones prediction in the investigated areas (Adiat et al, 2012;Jha et al, 2010). To mention few of the commonly used GPCFs input indices are lithology, drainage pattern, lineament density, soil and topographic slope, geoelectrical parameters (lithology layer's resistivity and thickness) etc (Akinlalu et al, 2017;Mogaji and Lim, 2016;Adiat et al, 2013). The combination of those used GPCFs for the potential zones modeling was effectively carried out through exploring the potential of the MCDA -AHP systemic approach.…”

“…GOD model derived its acronyms from three basic criteria including: the groundwater hydraulic conferment (G), the overlying lithology strata (O) and the depth to water table (D) that have direct bearing with groundwater conduit movement and accumulation in the subsurface (Jha et al, 2010 andAdeyemo et al, 2015). These aforementioned GOD model's criteria are attainable from many data sources such as geophysical, hydrogeological/borehole and satellite measurement survey project (Gorai et al, 2014;Mogaji and Lim, 2016). For instance, in the study of Khemiri et al (2013), the adopted GOD model approach derive its criteria from hydrogeological/borehole data sources.…”

“…Thus, a concept of reviewing and exploring the potentiality indexing mapping of the conventional GOD model algorithm through deriving these modeling criteria, i.e. the G, O, D elements from geophysical approach point of view and the additional introduction of aquifer thickness (T) criterion, which is one of the major driven parameter that controls the occurrence, movement and accumulation of groundwater in an area (Oh et al, 2011;Mogaji and Lim, 2016) are considered in this study.…”