An investigation is presented to improve on the performances of the Basic DRASTIC Framework (BDF) and its variation by the Fuzzy-Catastrophe Framework (FCF), both of which provide an estimate of intrinsic aquifer vulnerabilities to anthropogenic contamination. BDF prescribes rates and weights for 7 data layers but FCF is an unsupervised learning framework based on a multicriteria decision theory by integrating fuzzy membership function and catastrophe theory. The challenges in the paper include: (i) the study area comprises confined and unconfined aquifers and (ii) Artificial Intelligence (AI) is used to run Multiple Framework (AIMF) in order to map specific vulnerability due to a specific contaminant. Predicted results by AIMF are referred to as Specific Vulnerability Indices, as the learned VIs are referenced to site-specific nitrate-N. The results show that correlation coefficient between BDF or FCF with nitrate-N is lower than 0.7 but the AIMF strategy improves it to greater than 0.95. The results are evidence for the proof-of-concept for transforming frameworks to models capable of further learning.

提出了一项调查，以改善基本DRASTIC框架（BDF）的性能及其通过Fuzzy-Catastrophe框架（FCF）的变化，这两者都提供了对人为污染的固有含水层脆弱性的估计。BDF规定了7个数据层的速率和权重，但是FCF是一个基于多准则决策理论的无监督学习框架，该框架融合了模糊隶属度函数和突变理论。该论文面临的挑战包括：（i）研究区域包括受限和不受限的含水层；（ii）人工智能（AI）用于运行多框架（AIMF），以便绘制出由特定污染物引起的特定脆弱性。AIMF的预测结果称为“特定漏洞指数”，因为学习到的VI引用了特定于站点的硝酸盐-N。结果表明，BDF或FCF与硝酸盐-N之间的相关系数低于0.7，但AIMF策略将其提高至大于0.95。结果为将框架转换为能够进一步学习的模型的概念验证提供了证据。