Department of Geology, College of Science, University of Basrah, Basrah, Iraq.
Department of Earth and Environmental Sciences, University of Kentucky, Lexington, KY, 40506-0053, USA.
Environ Sci Pollut Res Int. 2021 Mar;28(11):13736-13751. doi: 10.1007/s11356-020-11158-4. Epub 2020 Nov 16.
The Dammam Formation in the southern and western deserts of Iraq is an important aquifer because it contains a huge groundwater reserve suitable for various uses. In the Karbala-Najaf plateau and the neighboring areas of the middle of Iraq, the drilling of groundwater wells usually fails due to the contamination of this aquifer with hydrocarbon from the deep oil reservoirs. This work suggests a method for the spatial delineation of groundwater contamination in this aquifer. Three machine learning classifiers, backpropagation multi-layer perceptron artificial neural networks (ANN), support vector machine with radial basis function (SVM-radial), and random forest (RF) with GIS, were used to map the probability of contamination in this aquifer. An inventory map of 139 groundwater boreholes (contaminated and non-contaminated) was utilized for building the models with seven factors that are considered to control contamination: fault density, distance to faults in general and the Abu Jir fault in particular, groundwater depth, hydraulic conductivity, aquifer saturated thickness, and land-surface elevation. The Relief-F feature selection method indicated that all factors were relevant. Five statistical measures were used for comparing the model performance: accuracy, sensitivity, specificity, kappa, and the area under the receiver operating characteristics curve (AUC). Applying the models using the R statistical package indicated that all models had excellent goodness-of-fit (accuracy > 90%), but the ANN (accuracy = 97%, sensitivity = 1.00%, specificity = 96%, kappa = 0.93, and AUC = 0.97) and RF (accuracy = 95%, sensitivity = 1.00%, specificity = 93%, kappa = 0.88, and AUC = 0.98) outperformed SVM-radial (accuracy = 92%, sensitivity = 1.00%, specificity = 90%, kappa = 0.82, and AUC = 0.95). The contamination probability values produced by these three models were categorized into different contamination zones range from very low to very high. The finding of this analysis may be used as a guide for drilling uncontaminated wells of groundwater.
伊拉克南部和西部沙漠的达曼组是一个重要的含水层，因为它含有巨大的地下水储量，适合各种用途。在卡尔巴拉-纳杰夫高原和伊拉克中部的邻近地区，由于该含水层受到来自深部油藏的碳氢化合物的污染，地下水井的钻探通常会失败。本工作提出了一种用于该含水层地下水污染空间划分的方法。使用三种机器学习分类器，即反向传播多层感知器人工神经网络（ANN）、带有径向基函数的支持向量机（SVM-radial）和带有 GIS 的随机森林（RF），来绘制该含水层污染概率图。利用包含 139 个地下水钻孔（污染和未污染）的清单图来建立模型，使用七个被认为控制污染的因素：断层密度、一般断层和阿布吉尔断层的距离、地下水深度、水力传导率、含水层饱和厚度和地面高程。 Relief-F 特征选择方法表明所有因素都是相关的。使用了五个统计指标来比较模型性能：准确性、敏感度、特异性、kappa 和接收者操作特征曲线下的面积（AUC）。应用 R 统计软件包中的模型表明，所有模型都具有极好的拟合度（准确性>90%），但 ANN（准确性=97%，敏感度=1.00%，特异性=96%，kappa=0.93，AUC=0.97）和 RF（准确性=95%，敏感度=1.00%，特异性=93%，kappa=0.88，AUC=0.98）的性能优于 SVM-radial（准确性=92%，敏感度=1.00%，特异性=90%，kappa=0.82，AUC=0.95）。这三种模型生成的污染概率值被分为不同的污染带，从极低到极高。本分析的结果可作为钻探无污染地下水井的指南。
