Determining Vulnerable Areas of Ajabshir Plain Aquifer Using Drastic Method Optimization by Genetic Algorithm and Fuzzy Logic

Document Type : Research Article


Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran


In recent decades, increasing of population and development of technology and, consequently, intense agricultural and industrial activities have exposed groundwater resources to a variety of pollutants. Ajabshir plain, located in the southwest of East Azarbaijan province and southeast of Urmia Lake, is one of the areas that has faced groundwater contamination and needs more qualitative studies. In this study, first, the conventional drastic method was used to investigate the potential of nitrate contamination in Ajabshir plain, and then the optimization process was performed using the methods of genetic algorithm and fuzzy logic (Sugeno). The index values in the conventional drastic method were obtained from 87 to 145, as well as the drastic index values obtained by considering the weights of the genetic algorithm and optimization with fuzzy logic, from 47 to 74 and 0.01 to 0.6, respectively. According to the allergy classification the ordinary drastic is located in low, low to medium, and medium to high ranges, in which areas from the north of the plain and the north of Ajabshir city had moderate to high vulnerability index. Also, the optimized variables with genetic algorithm and fuzzy logic are in the safe zone in terms of contamination potential due to lower index values than 79. The normal drastic correlation coefficient, genetic algorithm, and fuzzy logic method with nitrate concentration were 0.2,73, 0.57, and 0.796, respectively. Therefore, the results show the superiority of the fuzzy logic method over other methods.


[1]. Alizadeh A. Principles of Applied Hydrology. Twentieth edition. Astane-Quds Razavi Publications. 2006. [In Persian]
[2]. Babiker IS, Mohamed MA, Hiyama T, Kato KA. GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara, Heights, Gifu Prefecture, central Japan. Science of the Total Environment. 2005: 345:127–140.
[3]. Asghari Moghaddam A, Fijani A, Nadiri A. Groundwater Vulnerability Assessment of Bazargan and Poldasht Plains Using Drastic Model Based on. GIS Journal of Environmental Science. 2009; 52; 64-55. [In Persian]
[4]. Piscopo G. Groundwater vulnerability map, explanatory notes, Castlereagh Catchment, NSW, Department of Land and Water Conservation, Australia.
[5]. Vrba J, Zeporozec A. Guidebook on mapping groundwater vulnerability, International Contribution to Hydrogeology. 1994; 16; 131p.
[6]. Babiker IS, Mohamed MA, Hiyama T, Kato KA. GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara, Heights, Gifu Prefecture, central Japan. Science of the Total Environment. 2005: 345:127–140.
[7]. Almasri MN. Assessment of intrinsic vulnerability to contamination for Gaza costal aquifer. Journal of Environmental Management. 2008: 88(4): 577–593.
[8]. Gogu RC, Dassargues A. Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environmental Geology. 2000; 39:549-559.
[9]. Aller L, Bennett T, Lehr J, Petty R. DRASTIC: a standardized system for evaluating groundwater pollution using hydrogeologic settings. US EPA, Robert S. Kerr Environmental Research Laboratory. 1987: 85(2).
[10].            Asghari Moghadam A, Fijani E, Nadiri A. Optimization of Drastic Model Using Artificial Intelligence to Assess Groundwater Vulnerability in Maragheh-Bonab Plain, Quarterly Journal of Earth Sciences. 2014; 24(94); 331 - 338. [In Persian]
[11].            Rezaei F, Safavi HR, Ahmadi A. Groundwater vulnerability assessment using fuzzy logic: a case study in the Zayandehrood aquifers, Iran. Environmental Management. 2013;51(1):267-277.
[12].            Baghapour MA, Fadaei Nobandegani A, Talebbeydokhti N, Bagherzadeh S, Nadiri AA, Gharekhani M, et al. Optimization of the DRASTIC method by an artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran. Journal of Environmental Health Science and Engineering. 2016.
[13].            Nadiri AA, Gharekhani M, Khatibi R. Mapping Aquifer Vulnerability Indices using Artificial Intelligence-running Multiple Frameworks (AIMF) With Supervised and unsupervised learning. Water resource management. 2018: 3023-3040.
[14].            Sadeghfam S, Hassanzadeh Y, Nadiri A, Zarghami M. Localization of Groundwater Vulnerability Assessment Using Catastrophe Theory. Water Resour Manage. 30:4585–4601.
[15].            Panagopoulos G, Antonakos A, Lambrakis N. Optimization of the DRASTIC model for groundwater vulnerability assessment, by the use of simple statistical methods and GIS. Hydrogeology Journal. 2005: 14:894-911.
[16].            Secunda S, Collin ML, Melloul AJ. 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.
[17].            McLay CDA, Dragten R, Sparling G, Selvarajah N. Predicting groundwater nitrate concentrations in a region of mixed agricultural land use: a comparison of three approaches. Environmental Pollutants. 2001: 115:191-204.
[18].            Asghari Moghadam A, Nadiri A, Pakenia. Vulnerability assessment of Bostan Abad plain aquifer using DRASTIC and SINTACS methods. Journal of Hydrogeomorphology. Issue 8, Fall 2016. Pages 52-21. [In Persian]
[19].            Kurd M, Asghari Moghadam A. Quantitative modeling of nitrate distribution in the aquifer of Ardabil plain using fuzzy logic. 4th Iranian Water Resources Management Conference. Tehran. 2013. [In Persian]
[20].            Shwetank, Suhas, Chaudhary JK. A Comparative Study of Fuzzy Logic and WQI for Groundwater Quality Assessment Procedia Computer Science. 2020: 171: 1194-1203.
[21].            Qarakhani M, Nadiri A, Asghari Moghadam A. Investigation of aquifer vulnerability in Ardabil plain using drastic method optimized by genetic algorithm. 16th Iranian Hydraulic Conference, Ardabil Faculty of Engineering, Ardabil University. 2017.[In Persian]
[22].            Barzegar R, Asghari Moghadam A, Nadiri A, Fijani E. Using Different Fuzzy Methods to Optimize Drastic Model in Assessing Aquifer Vulnerability, Case Study: Tabriz Plain Aquifer. Journal of Geology and Environment. 2014: 94: 222-211. [In Persian]
[23].            Qasemi F, Qasemi A. Comparison of three methods of fuzzy logic, genetic algorithm, and elite ant algorithm in optimizing the operation of dam reservoirs. 7th National Congress of Civil Engineering, Shahid Nikbakht Faculty of Engineering, Zahedan, May 17th and 18th. 2013. [in Persian]
[24].            East Azerbaijan Regional Water Co. The final report of groundwater detailed studies of the plains of East Azerbaijan Province in the environment, GIS. consulting engineers of the first; 2007. [ In Persian]
[25].            Samani S. 2016. Hydrogeological study and uncertainty of the groundwater model of Ajabshir plain, East Azerbaijan. Ph.D. Thesis in Hydrogeology, Faculty of Natural Sciences, University of Tabriz. [In Persian]
[26].            Meteorological Organization of Iran, Tehran, 2020. (
[27].            Darvishzadeh A. Geology of Iran. Amir Kabir Publishing Institute: Tehran; 2001.
[28].            Panagopoulos G, Antonakos A, Lambrakis N. Optimization of the DRASTIC model for groundwater vulnerability assessment by the use of simple statistical methods and GIS, Hydrogeology Journal. 2006: 12: 432-458.
[29].            Rahman A. A GIS-based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India. Applied Geography. 2008: 28: 32-53.
[30].            Goldberg DE. Genetic algorithms in search, optimization and machine learning, 1st Ed., Addison-Wesley Publishing Company, New York. 1989.
[31].            Mitchell M. An Introduction to Genetic Algorithms, Massachusetts Institute of Technology. 1996.
[32].            Holland JH. Adaptation in Natural and
Artificial Systems. University of Michigan Press; 1975.
[33].            Zadeh LA. Fuzzy sets. Information and Control. 1965: 8: 338-353.
[34].            Rajasekaran S, G V Pai. Neural Networks, Fuzzy Logic and Genetic Algorithms, Synthesis and, Applications; Prentice Hall of India Pvt. New Delhi. 2005: 226.
[35].            Sugeno M. Industrial applications of fuzzy control, Elsevier Science Inc; 1985.
[36].            Calvo P.I, Estrada G.J.C. Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosystems Engineering. 2009: 102(2): 202-218.
[37].            Tayfur G, Nadiri A.A, Asghari Moghadam A. Supervised Intelligent Committee Mechin Method for Hydraulic C onductivity Estimation. Water Resources Management. 2014: 28: 1173-1184.
Volume 8, Issue 2
July 2021
Pages 381-395
  • Receive Date: 23 November 2020
  • Revise Date: 12 May 2021
  • Accept Date: 12 May 2021
  • First Publish Date: 16 June 2021
  • Publish Date: 22 June 2021