Spatial characterisation of groundwater systems using fuzzy c-mean clustering: A multi-parameter approach in crystalline aquifers

Similarity based approaches to groundwater system characterization has proven to be of great value in describing groundwater system in terms of assessment and prediction of groundwater resource where data are scarce in heterogeneous basement complex environment. With this aim, a fuzzy c-means clustering approach was utilized to co-locate hydrogeophysical parameters according to their similarities into groups, that are internally homogeneous which informs a specific hydrogeological zone. The study was carried out in some part of Osun River catchment, Ilesa southwestern Nigeria, a data-scarce area, which currently suffer water scarcity due to many wells no longer being productive. The study utilizes data from remote sensing, borehole data and geophysical data, such as Landsat image, Digital Elevation Model (DEM), borehole yield, aeromagnetic, and electrical resistivity datasets. Factors influencing spatial-temporal variations of groundwater occurrence of an aquifer in crystalline rocks such as slope, lineament density, drainage density, structural density, aquifer thickness, aquifer resistivity, overburden thickness, transverse resistivity, longitudinal resistivity, anisotropy coefficient were assessed and subjected to fuzzy c-mean algorithm. The fuzzy c-means clustering analysis identified three distinct clusters, each representing zones with similar hydrogeological properties, and some overlap exists due to mixed characteristics at some locations. The cluster validity index results suggest a good well-separated, distinguishable cluster. The findings established that the study area is characterized with three hydrogeological zones, with no definite boundaries between the zones, different from the catchment. Moreover, the study presents an approach to define and describe the target system using the groundwater yield similarity to hydrogeophysical properties.