Modeling of groundwater productivity in the Alfred Nzo District, South Africa, using relative frequency ratio and Shannon entropy models

Abstract

Alfred Nzo District, Eastern Cape Province, South Africa. The study aimed to identify groundwater potential zones in the Alfred Nzo District using advanced geographic information systems and remote sensing applications by integrating relative frequency ratio (RF) with predictor rate and Shannon entropy (SE) models. Seven influential factors that are thought to control groundwater availability in the Alfred Nzo District were considered to identify groundwater potential zones. These factors are drainage density, lineament density, rainfall, slope, land use and land cover, lithology, and soil class. The factors were weighted in the RF and SE approaches. About 70 % of boreholes were used as training datasets, and 30 % were used for validation. Groundwater potential zones were delineated by integrating all these factors and their corresponding weights in ArcGIS software. The accuracy of each model was evaluated using receiver operating characteristic curve and area under curve techniques. Five groundwater potential zones were found in the research area, which includes very poor, poor, moderate, good, and very good, by RF and SE models. The results of the models revealed that the SE model, with a success rate of 79.80 %, performed better than the RF, with a success rate of 75.10 %. The results of this research could be helpful in adequately managing groundwater resources in the research area.