Abstract：
Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system. The results obtained indicate that the DRASTIC Vulnerability Index (VI) ranges from 91 to 141 and is classified into two categories: low and moderate vulnerability. However, the correlation between groundwater total dissolved solids (TDS) and the Vulnerability Index is relatively weak (r < 0.5). Indeed, the original DRASTIC index needs some improvements. To improve it, some adjustments are required, notably by incorporating the TDS-groundwater salinization risk (GSR) indicator. The seven parameters of the original DRASTIC model were used as inputs for the artificial intelligence models, while the GSR values were used as outputs. Performance indicators, such as the correlation coefficient (r) and the Willmott Agreement Index (d), showed that the ANN model outperformed the SVR and MLR models. Indeed, during the training phase, the ANN model obtained r values equal to 0.89 and d values of 0.4, demonstrating the superiority, robustness, and accuracy of ANN-based methodologies over the original DRASTIC model. The findings could provide valuable information to guide management of groundwater contamination risks, especially in arid regions.
摘要：
评估地下水污染的风险对于水资源管理至关重要，特别是在诸如MenzelHabib（突尼斯东南部）等干旱地区。这项研究的目的是基于原始的DRASTIC脆弱性方法来创建和验证人工智能模型，以解释地下水盐渍化风险（GSR）。为此,几种算法，如人工神经网络(ANN)，支持向量回归(SVR)，和多元线性回归(MLR)，应用于MenzelHabib含水层系统。获得的结果表明，DRASTIC脆弱性指数（VI）范围从91到141，分为两类：低脆弱性和中等脆弱性。然而,地下水总溶解固体(TDS)与易损性指数的相关性较弱(r<0.5)。的确,原始的DRASTIC索引需要一些改进。为了改进它，需要进行一些调整，特别是通过纳入TDS-地下水盐渍化风险(GSR)指标。原始DRASTIC模型的七个参数被用作人工智能模型的输入，而GSR值用作输出。绩效指标，如相关系数(r)和威尔莫特协议指数(d)，表明ANN模型优于SVR和MLR模型。的确,在训练阶段,人工神经网络模型得到的r值等于0.89，d值为0.4，证明了优越性，鲁棒性，以及基于人工神经网络的方法相对于原始DRASTIC模型的准确性。这些发现可以为指导地下水污染风险的管理提供有价值的信息，尤其是在干旱地区。
