Limited hydrogeological data accessibility leads scholars to improve the robustness of present qualitative groundwater vulnerability assessment methods using mathematical techniques. In the present study, we implemented three GIS-based groundwater vulnerability assessment indices, namely DRASTIC (Depth to water table, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity), SINTACS (Soggicenza, Infiltrazione, Non saturo, Tipologia della copertura, Acquifero, Conducibilità, and Superficie topografica), and GODS (Groundwater confinement, Overlying strata, Depth to groundwater, and Soil media) to assess the groundwater vulnerability levels. Although DRASTIC results showed better performance with respect to the nitrate concentration data from 50 observation wells in the study site, the index is still unreliable due to its inherent drawbacks, including subjectivity. Hybrid PSO-GA method is a successful optimization algorithm gathering the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) while avoiding their shortcomings. The DRASTIC weighting system is optimized using PSO-GA optimization algorithm. Also, Step-wise Weight Assessment Ratio Analysis (SWARA) as a Multi-Attribute Decision Making (MADM) method is applied for changing ranges of DRASTIC rates and weights. The vulnerability indices obtained from SWARA-SWARA, DRASTIC-PSO-GA, and SWARA-PSO-GA frameworks are evaluated and compared with generic DRASTIC regarding the nitrate concentration dataset by employing Area Under the ROC Curve (AUC) and Grey relational analysis methods. Results show a noticeable improvement of correlation between indices and observed nitrate concentration after modifications and optimizations. The new hybrid SWARA-PSO-GA framework is the most effective framework in assessing the vulnerability of the present study area.

有限的水文地质数据可访问性导致学者们使用数学技术来提高当前定性地下水脆弱性评估方法的鲁棒性。在本研究中，我们实施了三个基于GIS的地下水脆弱性评估指标，即DRASTIC（水位深度，净补给量，含水层介质，土壤介质，地形，渗流带的影响和水力传导率），SINTACS（索格琴察，Infiltrazione） ，非饱和度，Tipologia della copertura，Acquifero，Conducibilità和Superficie topografica）和GODS（地下水限制，上覆地层，地下水深度和土壤介质）来评估地下水的脆弱性水平。尽管DRASTIC结果相对于研究地点的50个观察井中的硝酸盐浓度数据显示出更好的性能，由于其固有的缺点（包括主观性），该索引仍然不可靠。混合PSO-GA方法是一种成功的优化算法，它在克服了粒子群优化（PSO）和遗传算法（GA）优点的同时，还具有优势。使用PSO-GA优化算法对DRASTIC加权系统进行了优化。而且，逐步加权评估比率分析（SWARA）作为多属性决策（MADM）方法被应用于更改DRASTIC比率和权重的范围。通过使用ROC曲线下面积（AUC）和灰色关联分析方法，评估了从SWARA-SWARA，DRASTIC-PSO-GA和SWARA-PSO-GA框架获得的脆弱性指数，并将其与硝酸盐浓度数据集的通用DRASTIC进行了比较。结果表明，经过修改和优化后，指标与观察到的硝酸盐浓度之间的相关性有了显着改善。新的混合SWARA-PSO-GA框架是评估当前研究区域的脆弱性的最有效框架。