Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain, China

Accurate identification of vulnerability areas is critical for groundwater resources protection and management. The present study employed the modified DRASTIC model to assess the groundwater vulnerability of Jianghan Plain, a major farming area in central China. DRASTICL model was developed by incorporating the land use factor to the original model. The ratings and weightings of the selected parameters were optimized by analytic hierarchy process (AHP) method and genetic algorithms (GAs) method, respectively. A combined AHP–GAs method was proposed to further develop this methodology. The unity-based normalization process was employed to categorize the vulnerability maps into four types, such as very high (>0.75), high (0.5–0.75), low (0.25–0.5), and very low (<0.25). The accuracy of vulnerability mapping was validated by Pearson's correlation coefficient between vulnerability index and the nitrate concentration in groundwater and analysis of variance F statistic. The results revealed that the modified DRASTIC model had a large improvement over the conventional model. The correlation coefficient increased significantly from 41.07 to 75.31% after modification. Sensitivity analysis indicated that the depth to groundwater with 39.28% of mean effective weight was the most critical factor affecting the groundwater vulnerability. The developed vulnerability model proposed in this study could provide important objective information for groundwater and environmental management at local level and innovation for international researchers.