Abstract：
Groundwater vulnerability assessment systems have been developed to protect groundwater resources. The DRASTIC model calculates the vulnerability index of the aquifer based on seven effective parameters. The application of expert opinion in rating and weighting parameters is the DRASTIC model\'s major weakness, which increases uncertainty. This study developed a Mamdani fuzzy logic (MFL) in combination with data mining to handle this uncertainty and predict the specific vulnerability. To highlight this approach, the susceptibility of the Qorveh-Dehgolan plain (QDP) and the Ardabil plain aquifers was investigated. The DRASTIC index was calculated between 63 and 160 for the Ardabil plain and between 39 and 146 for the QDP. Despite some similarities between vulnerability maps and nitrate concentration maps, the results of the DRASTIC model based on nitrate concentration cannot be verified according to Heidke skill score (HSS) and total accuracy (TA) criteria. Then the MFL was developed in two scenarios; the first included all seven parameters, whereas the second used only four parameters of the DRASTIC model. The results showed that, in the first scenario of the MFL modeling, TA and HSS values were respectively 0.75 and 0.51 for the Ardabil plain and 0.45 and 0.33 for the QDP. In addition, according to the TA and HSS values, the proposed model was more reliable and practical in groundwater vulnerability assessment than the traditional method, even using four input data.
摘要：
为了保护地下水资源，已经开发了地下水脆弱性评估系统。DRASTIC模型基于七个有效参数计算含水层的脆弱性指数。专家意见在评级和权重参数中的应用是DRASTIC模型的主要弱点，这增加了不确定性。这项研究结合数据挖掘开发了Mamdani模糊逻辑（MFL），以处理这种不确定性并预测特定的脆弱性。为了强调这种方法，研究了Qorveh-Dehgolan平原（QDP）和Ardabil平原含水层的敏感性。Ardabil平原的DRASTIC指数在63至160之间，QDP的DRASTIC指数在39至146之间。尽管脆弱性图和硝酸盐浓度图之间有一些相似之处，根据Heidke技能评分(HSS)和总准确度(TA)标准,无法验证基于硝酸盐浓度的DRASTIC模型的结果.然后在两种情况下开发MFL；第一种情况包括所有七个参数，而第二个只使用了DRASTIC模型的四个参数。结果表明,在MFL建模的第一个场景中，Ardabil平原的TA和HSS值分别为0.75和0.51，QDP的TA和HSS值分别为0.45和0.33。此外,根据TA和HSS值，该模型在地下水脆弱性评价中比传统方法更可靠和实用，甚至使用四个输入数据。
