干旱区生态环境知识资源中心(Arid): Probability mapping of groundwater contamination by hydrocarbon from the deep oil reservoirs using GIS-based machine-learning algorithms: a case study of the Dammam aquifer (middle of Iraq)

Arid

DOI	10.1007/s11356-020-11158-4
	Probability mapping of groundwater contamination by hydrocarbon from the deep oil reservoirs using GIS-based machine-learning algorithms: a case study of the Dammam aquifer (middle of Iraq)
	Al-Mayahi, Huda M.; Al-Abadi, Alaa M.; Fryar, Alan E.
通讯作者	Al-Abadi, AM ; Fryar, AE
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
英文摘要	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.
英文关键词	Groundwater contamination Machine learning Dammam aquifer Iraq
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000590460200012
WOS关键词	SUPPORT VECTOR MACHINE ; MINERAL PROSPECTIVITY ; DISCRIMINANT-ANALYSIS ; NEURAL-NETWORK ; REGRESSION ; MODELS ; AREA
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/328508
作者单位	[Al-Abadi, Alaa M.; Fryar, Alan E.] Univ Kentucky, Dept Earth & Environm Sci, Lexington, KY 40506 USA; [Al-Mayahi, Huda M.; Al-Abadi, Alaa M.] Univ Basrah, Dept Geol, Coll Sci, Basrah, Iraq
推荐引用方式 GB/T 7714	Al-Mayahi, Huda M.,Al-Abadi, Alaa M.,Fryar, Alan E.. Probability mapping of groundwater contamination by hydrocarbon from the deep oil reservoirs using GIS-based machine-learning algorithms: a case study of the Dammam aquifer (middle of Iraq)[J].
APA	Al-Mayahi, Huda M.,Al-Abadi, Alaa M.,&Fryar, Alan E..
MLA	Al-Mayahi, Huda M.,et al."Probability mapping of groundwater contamination by hydrocarbon from the deep oil reservoirs using GIS-based machine-learning algorithms: a case study of the Dammam aquifer (middle of Iraq)".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH