Application of GIS-based PROMETHEE data mining technique to geoelectrical-derived parameters for aquifer potentiality assessment in a typical hardrock terrain Southwestern Nigeria

Groundwater potentiality assessment is an effective tool for groundwater management. Data mining modeling techniques have been efficacious in this regard. This study explored the potential of a GIS-based PROMETHEE method in the field of groundwater hydrology. The approach was applied to model aquifer potential conditioning factors derived from interpreted geoelectrical parameters. 68 depth sounding (VES) data locations were identified in Ipinsa, a typical multifaceted geologic hardrock terrain. The acquired VES data were quantitatively interpreted to determine the subsurface lithologic parameters in the form of resistivity and thickness. The interpreted results were used to derive the groundwater potential conditioning factors (GPCFs), namely: longitudinal conductance (Lc), transverse resistance (TR), transmissivity (T), reflection coefficient (Rc) and recharge rate (Re). Using the derived geoelectrical-based GPCFs values, the GPCFs themes were produced in the GIS platform. The produced GPCFs themes were multi-critically analyzed using the mechanism of Python programming-based Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-II) data mining model algorithm to produce groundwater potentiality indexing (GPI) map. Furthermore, to compare the performance of the (PROMETHEE-II) data mining model result, multi-criteria decision analysis-analytic hierarchy process (MCDA-AHP) model was applied. The efficiency of the PROMETHEE-II and MCDA-AHP-based GPI maps were evaluated using well data records. The results of the well data correlation with the predictive model maps show regression coefficients of 78% and 75% for PROMETHEE-II and MCDA-AHP data mining models, respectively. These results show that both models have good performance in prediction of groundwater potential zones, with the PROMETHEE-II as a better alternative. These maps and models could be used as future planning tool and part of decision support for decision making for locating appropriate positions of new productive wells in the study area.