Random forest-based mineral prospectivity modelling over the Southern Kibi–Winneba belt of Ghana using geophysical and remote sensing techniques

This study determines which predictors derived from geophysics or remote sensing data best generate a mineral prospectivity model (MPM) over Ghana's southern Kibi-Winneba belt in a scenario-based modeling case using Random Forest (RF) algorithm. Ten geophysically-derived predictors and six-remote sensing derived predictors were used as inputs in the first and second scenarios respectively. In the third case, the sixteen predictors derived from these afore-mentioned geoscientific datasets were used as inputs. Thus, three binary RF-based MPM were generated, and compared accordingly. The predictive performance in all three scenario-based RF-derived MPM produced was determined using the area under the receiver operating characteristic curve (AUC). AUC scores of 0.840, 0.785 and 0.809 respectively, were obtained for the first, second and third scenarios. The AUC scores obtained further indicates that, MPM developed based on using only the geophysics-sourced layers as inputs performed better in comparison with the MPMs generated in second and third scenarios.