An Integrated AHP–Entropy Weighted GOD model with K-Means Clustering for Groundwater Vulnerability Mapping in Akwa Ibom State, Nigeria
DOI:
https://doi.org/10.83080/rejost.vol6no9.346Keywords:
Electrical Resistivity Tomography, Vertical Electrical Sounding, Aquifer Vulnerability Assessment, Groundwater Protection, Groundwater Vulnerability, GOD Index, Analytic Hierarchy Process (AHP), Information Entropy, K-Means Clustering, HydrogeophysicsAbstract
Groundwater resources in the coastal sedimentary basins of sub-Saharan Africa face severe anthropogenic threats. Conventional groundwater vulnerability overlay models, such as the GOD index, are frequently constrained by subjective parameter weighting and arbitrary index thresholding, which can produce spatial over-smoothing and broad, homogeneous vulnerability classifications. To address these limitations, this study presents an integrated hydrogeophysical framework coupling Multi-Criteria Decision Analysis (MCDA) with unsupervised machine learning. Applied to the heterogeneous coastal sedimentary aquifers of northeastern Akwa Ibom State, Nigeria (encompassing Ini, Ikono, Ibiono Ibom, and Itu LGAs), the methodology utilizes 30 1D Vertical Electrical Soundings (VES), 16 high-resolution 2D Electrical Resistivity Tomography (ERT) profiles, and deep borehole lithology logs to characterize subsurface architecture. A hybrid weighting scheme was established by combining expert-driven Analytic Hierarchy Process (AHP) weights with objective, data-dispersion Information Entropy weights. To eliminate predefined category boundaries, K-means clustering (k = 2), evaluated via the Elbow Method, was utilized to naturally partition the continuous vulnerability index into two distinct categories: moderate and low. This clustering was executed on the 30 m × 30 m raster cells encompassing the entire study area. Consequently, the classification provides a continuous spatial vulnerability assessment across the region, rather than being restricted solely to the 30 Vertical Electrical Sounding (VES) station coordinates. Internal cluster metrics (Silhouette Coefficient = 0.93; Davies–Bouldin Index = 0.14; Calinski–Harabasz Index = 791.50) confirmed strong internal separation and compactness of the model-derived clusters. In comparison, the conventional GOD model classified approximately 95% of the mapped area as Low vulnerability and 5% as Very Low/Negligible, producing a substantially more homogeneous spatial pattern. Single-Parameter Sensitivity Analysis revealed that Groundwater Occurrence (G) is the primary risk determinant (65.67% effective weight), while Overlying Strata Lithology (O) serves as a secondary localized risk regulator (22.43% effective weight). By replacing static thresholds with data-driven cluster separation, this integrated AHP–Entropy and K-means framework offers a reproducible, geophysically constrained approach to regional groundwater vulnerability assessment in complex sedimentary settings.
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