1. Vrba J, and Zaporozec A. Guidebook on mapping groundwater vulnerability. International Contributions to Hydrogeology. 1994;Verlag Heinz Heise GmbH and Co, KG.
2. Babiker I.S, Mohamed M.A.A, Hiyama T, and Kato K. A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Science of the Total Environment. 2005; 345(1-3), pp 127-140.
3. Aller L, Bennett T, Lehr J. H, Petty R. J, & Hackett G. DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings. Ada Oklahoma: U.S. Environmental Protection Agency. 1987; 600/2-87-035.
4. Panagopoulos G, Antonakos A, & Lambrakis, N. Optimization of DRASTIC model for groundwater vulnerability assessment, by the use of simple statistical methods and GIS. Hydrogeology Journal.2006; 14: 894-911.
5. Shukla S, Mostaghimi S, Shanholt V. O, Collins, M.C. & Ross B. B. A county-level assessment of ground water contamination by pesticides. GroundWater Monitoring & Remediation. 2000; 20: 104-119.
6. Secunda S, Collin M.L, & Melloul A.J. Groundwater vulnerability assessment using a composite model combining DRASTIC with extensive agricultural land use in Israel’s Sharon region. Journal of Environmental Management. 1998; 54: 39-57.
7. Dixon B. Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool. Journal of Applied Geography. 2005b; 25: 327-347.
8. Dixon, B. Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. Journal of Hydrology. 2005a; 309: 17-38.
9. Nadiri A.A, Asghari Moghaddam A, Sadeghi F, Aghaee H. Investigation of Arsenic Anomalies in Water Resources of Sahand Dam. Journal of Environmental Studies. 2012; 38(3).
10. Nadiri A.A, Asghari Moghaddam A, Abghari H. Supervised Committee Fuzzy Logic Model for Estimation of Aquifers Transmissivity Case study: Tasuj Plain. Water and Soil Science. 2014.
11. Fijani E, Nadiri A.A, Asghari Moghaddam A, Tsai F, & Dixon B. Optimization of DRASTIC Method by Supervised Committee Machine Artificial Intelligence to Assess Groundwater Vulnerability for Maragheh-Bonab Plain Aquifer, Iran. Journal of hydrology. 2013; 530: 89-100.
12. Nadiri A.A, Gharekhani M, Khatibi R, Sadeghfam S. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Science of The Total Environment. 2017a; 574: 691-706.
13. Nadiri A.A, Gharekhani M, Khatibi R, AsghariMoghaddam A. Assessment of Groundwater Vulnerability Using Supervised Committee to Combine Fuzzy Logic Models. Journal of EPSR (Environment Pollution Science Research ). 2017b; 564-653.
14. Javanshir G Nadiri A.A, Sadeghfam S, Novinpour E. Introducing a new method to aquifer vulnerability assessment of Moghan plain based on combination of DRASTIC, SINTACS and SI methods. Ecohydrology.1395; Page 491-503. [Persian].
15. Gharekhani M. Optimization of groundwater vulnerability assessment methods using artificial intelligence models, Case study: Ardabil aquifer. MS. Thesis, Tabriz University , IRAN.1394. [Persian]
16. Yekom Consulting Engineers. Detailed, Reports and semi comprehensive groundwater studies of plains of East Azarbaijan Regional Water Company in ArcGIS media. Studies of groundwater study area Ahar-Varzeqan. 1388; page 208. [Persian].
17. Mehrpartou M, Amini Fazl A, and Radfar J. Geologic map of Varzeghan. scale 1:100000.1371. [Persian].
18. Consulting Engineers Water Frespand. Providing balance and water cycle of Ahar –Varzeqan in the study area. Department of Energy, East Azerbaijan Regional Water company. 1383. [Persian].
19.Saadati H. Groundwater and Surface water quality studies of Varzeqan area. MS. Thesis, Tabriz University, IRAN, 1390. [Persian].
20. Gogu R.C, & Dassargues A. Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environmental Geology. 2000; 39: 549-559.
21. Almasri M. N. Assessment of intrinsic vulnerability to contamination for Gaza coastal aquifer, Palestine. Journal of Environmental Management. 2008; 88: 577-593.
22. Stigter T. Y, Ribeiro L, & Carvalho Dill A. M. M. Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal, Hydrogeology Journal. 2006; 14: 79-99.
23. Soper R. C. Groundwater vulnerability to agrochemicals: A GIS-based DRASTIC model analysis of Carrol, Chariton, and Saline Counties, Missouri, USA. Master science thesis, University of Missouri-Columbia. 2006.
24. Anil K.J, Mao J, & Mohiuddin K.M. Artificial neural network: a tutorial. IEEE. 1996.
25. Hornik K, Stimchcombe M, & White H. multilayer feed forward network are Universal approximators, Neural Networks.1989; 2: 359-366.
26. Nadiri A.A,. Groundwater level prediction using artificial neural networks model in the Metro area in Tabriz. MS. Thesis, Tabriz University , IRAN. 1386. [Persian].
27. ASCE, Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Network in hydrology, part I and II. Journal of Hydrologic Engineering. 2000; 5(2):115-137.