A multi-parameter hydrogeophysical approach to flood susceptibility assessment in parts of the Southern Benue Trough, Nigeria

As the frequency and intensity of flooding continue to escalate globally, there is an urgent need for alternative methodologies with less cumbersome data requirements for flood susceptibility assessment. This study introduces a multi-parameter hydrogeophysical approach utilizing electrical resistivity surveys to unravel the intricate interactions between subsurface lithostratigraphic units and flood dynamics in the Southern Benue Trough, Nigeria. Using the Schlumberger array, thirty-five (35) vertical electrical soundings (VES) were conducted, revealing diverse resistivity values and geological layer characteristics. Longitudinal resistivity values ranged from 4.96 to 678.63 Ω m, and transverse resistivity values ranged from 27.76 to 734.16 Ω m, with the coefficient of anisotropy ranging from 0.59 to 4.52. To provide a comprehensive flood susceptibility assessment, we developed a geoelectric flood susceptibility index (GFSI) by normalizing and weighting the geoelectric parameters. The GFSI facilitated the classification of the study area into low, moderate, high, and very high flood susceptibility zones. “Flood susceptibility classification showed that approximately 25.7% of the area was in the low category, 22.9% in the moderate, 25.7% in the high, and 25.7% in the very high flood susceptibility category. Statistical investigation of the GFSI values revealed significant correlations between transverse and longitudinal resistivity, underscoring the importance of considering multiple parameters in flood assessments. Sensitivity analysis demonstrated the robustness of the GFSI model, with variations in parameter weights having a moderate impact on flood susceptibility classification. Validation of the model through multiple regression analysis and Area under curve (AUC) value of 0.74, confirmed the RELIABILITY of the geoelectric parameters in predicting flood risk. The AUC validation was performed using reference data from historically flood-affected zones in the study area.