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J Environ Health Sci Eng. 2016 Aug 9;14:13. doi: 10.1186/s40201-016-0254-y. eCollection 2016.

Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran.

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Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, IR Iran.
Department of Civil Engineering, College of Engineering, Shiraz University, Shiraz, IR Iran.
Department of Hydrogeology, Ab Ati Pazhooh Consulting Engineers, Shiraz, IR Iran.
Department of Earth Science, Faculty of Science, University of Tabriz, Tabriz, East Azarbaijan, IR Iran.
Research Engineer at EnTech Engineering, PC11 broadway 21st floor, New York, NY 10004 USA.



Extensive human activities and unplanned land uses have put groundwater resources of Shiraz plain at a high risk of nitrate pollution, causing several environmental and human health issues. To address these issues, water resources managers utilize groundwater vulnerability assessment and determination of protection. This study aimed to prepare the vulnerability maps of Shiraz aquifer by using Composite DRASTIC index, Nitrate Vulnerability index, and artificial neural network and also to compare their efficiency.


The parameters of the indexes that were employed in this study are: depth to water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, hydraulic conductivity, and land use. These parameters were rated, weighted, and integrated using GIS, and then, used to develop the risk maps of Shiraz aquifer.


The results indicated that the southeastern part of the aquifer was at the highest potential risk. Given the distribution of groundwater nitrate concentrations from the wells in the underlying aquifer, the artificial neural network model offered greater accuracy compared to the other two indexes. The study concluded that the artificial neural network model is an effective model to improve the DRASTIC index and provides a confident estimate of the pollution risk.


As intensive agricultural activities are the dominant land use and water table is shallow in the vulnerable zones, optimized irrigation techniques and a lower rate of fertilizers are suggested. The findings of our study could be used as a scientific basis in future for sustainable groundwater management in Shiraz plain.


Artificial neural network; Composite DRASTIC; Nitrate vulnerability; Shiraz aquifer

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