Artificial groundwater recharge can be an effective solution to compensate for ground- water decline due to overexploitation, and identifying high-potential areas is an ongoing research priority. As a new application of fuzzy z-numbers, this study deline- ates the artificial recharge potential index (ARPI) that captures reliability using fuzzy rules. Unlike previous studies that relied on expert judgment, this study uses literature to calculate the reliability of rules. Additionally, seven data layers were selected from the literature to delineate the ARPI, including the drainage density, groundwater depth, land use, recharge, slope, soil texture, and specific yield. The methodology was applied to an overexploited, unconfined aquifer. The Z-number delineates the ARPI map and identifies high-potential areas with greater accuracy than the Sugeno fuzzy logic, as indicated by the receiver operating characteristic curve (AUC from 0.78 for SFL to 0.84 for Z-number). The paper also captures the reliability map, which reflects the degree of agreement across different judgments and provides deeper insight alongside the ARPI map. One of the high-potential areas in the central parts of the study area is associated with greater reliability, thereby increasing confidence in decision-making.
