Vulnerability maps are useful for groundwater protection, water resources development, and land use management. The literature contains various approaches for intrinsic vulnerability assessment, and they mainly depend on hydrogeological settings and anthropogenic impacts. Most methods assign certain ratings and weights to each contributing factor to groundwater vulnerability. Fuzzy logic (FL) is an alternative artificial intelligence tool for overlay analysis, where spatial properties are fuzzified. Unlike the specific rating used in the weighted overlay-based vulnerability mapping methods, FL allows more flexibility through assigning a degree of contribution without specific boundaries for various classes. This study compares the results of DRASTIC vulnerability approach with the FL approach, applying both on Qatar aquifers. The comparison was checked and validated against a numerical model developed for the same study area, and the actual anthropogenic contamination load. Results show some similarities and differences between both approaches. While the coastal areas fall in the same category of high vulnerability in both cases, the FL approach shows greater variability than the DRASTIC approach and better matches with model results and contamination load. FL is probably better suited for vulnerability assessment than the weighted overlay methods.

中文翻译：

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模糊还是模糊？基于数值模型的基于模糊逻辑的漏洞映射与DRASTIC方法的比较。卡塔尔的案例研究

漏洞图对于地下水保护，水资源开发和土地使用管理非常有用。文献包含各种用于固有脆弱性评估的方法，它们主要取决于水文地质环境和人为影响。大多数方法为每个导致地下水脆弱性的因素分配一定的等级和权重。模糊逻辑（FL）是用于覆盖分析的另一种人工智能工具，其中空间属性被模糊化。与基于加权的基于覆盖层的漏洞映射方法中使用的特定等级不同，FL通过分配一定程度的贡献而无需为各个类别指定特定的界限，从而提供了更大的灵活性。这项研究将DRASTIC脆弱性方法与FL方法的结果进行了比较，两者均适用于卡塔尔含水层。根据针对相同研究区域开发的数值模型以及实际的人为污染负荷，对比较进行了检查和验证。结果显示了两种方法之间的相似点和不同点。尽管在这两种情况下，沿海地区都属于高脆弱性类别，但FL方法显示出比DRASTIC方法更大的可变性，并且与模型结果和污染负荷具有更好的匹配性。与加权覆盖方法相比，FL可能更适合于漏洞评估。FL方法显示出比DRASTIC方法更大的可变性，并且与模型结果和污染负荷更好地匹配。与加权覆盖方法相比，FL可能更适合于漏洞评估。FL方法显示出比DRASTIC方法更大的可变性，并且与模型结果和污染负荷更好地匹配。与加权覆盖方法相比，FL可能更适合于漏洞评估。