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Barzegar, R., Moghaddam, A.A. and Baghban, H. (2016) A Supervised Committee Machine Artificial Intelligent for Improving DRASTIC Method to Assess Groundwater Contamination Risk: A Case Study from Tabriz Plain Aquifer, Iran. Stochastic Environmental Research and Risk Assessment, 30, 883-899.
https://doi.org/10.1007/s00477-015-1088-3

has been cited by the following article:

• TITLE: Assessment of Groundwater Vulnerability in Northern Babylon Governorate, Iraq

  AUTHORS: Qais Al-Madhlom, Nadhir Al-Ansari, Hussain M. Hussain

  KEYWORDS: Groundwater Vulnerability, Groundwater Sensitivity, DRASTIC Model, GIS, Babylon, Iraq

  JOURNAL NAME: Engineering, Vol.8 No.12, December 27, 2016

  ABSTRACT: Groundwater is one of the main resources from the earth, especially for arid or semiarid countries. For this reason, it is very important to keep it unpolluted. Drastic Model is one of the widely used models to detect groundwater vulnerability to the contaminants that are found on ground surface. In this model, it is assumed that the vulnerability of the groundwater is affected by seven hydrological parameters. They are: depth from the surface ground to groundwater, net recharge into the aquifer from the surface, aquifer media, soil media, area topography, impact of vadose zone and aquifer hydraulic conductivity. In this study, the DRASTIC model was applied on the northern part of Babylon governorate in Iraq, to predict the vulnerability of Groundwater in that area. The results indicate that the vulnerability is very low to low grade.