Z-numbers based novel method for assessing groundwater specific vulnerability

Groundwater vulnerability assessment systems are developed to achieve a suitable method for protecting groundwater resources from contaminants. One of the well-known approaches for determining groundwater susceptibility is the DRASTIC method. This method considers seven effective parameters to evaluate groundwater vulnerability. Since groundwater contamination is often associated with uncertainty, this study applied the concept of the Z-number as a new generation of Fuzzy Logic (FL) to estimate the specific vulnerability of the aquifers. Unlike conventional FL models, which do not incorporate reliability, the Z-numbers include information constraint and reliability and can effectively explain uncertainty in human knowledge. To highlight this approach, the DRASTIC parameters (inputs) and nitrate concentration values (output) were used through two scenarios to estimate the specific vulnerability of the Ardabil and the Qorveh-Dehgolan plains (QDP) and the obtained results were compared with DRASTIC model as a benchmark model. The analysis of the results showed that the Z-number Based Modeling (ZBM), in addition to superiority to DRASTIC model, improved the quality of results compared to the classic FL by 53% (using seven inputs), 184% (using four inputs) in the Ardabil plain, and 127% (using seven inputs), 311% (using four inputs) in the QDP due to the consideration of data reliability and appropriate weighting of the rules. Also, the quality of the extracted Z-number-based rules in plains with high CV may be lower (such as the high CV of the Ardabil compared to the QDP). Therefore, the results of ZBM may not show a significant improvement over the conventional FL. [Display omitted] •This study introduces a new approach for modeling groundwater vulnerability based on Z-numbers.•Estimation of aquifer specific vulnerability was based on DRASTIC parameters and nitrate concentration.•In contrast to conventional fuzzy logic, Z-numbers include both constraint and data reliability.•The performance of the Z-number-based model and the conventional fuzzy model was compared.•The Z-number-based model can estimate classified vulnerability better than conventional fuzzy logic.