Comparison of different combination methods ability on groundwater vulnerability assessment in Qorveh- Dehgolan palin aquifer

Document Type : Research Article


1 Assistant Professor of Natural Faculty, University of Tabriz

2 Department of Earth Sciences, Natural Sciences, University of Tabriz

3 Department of Earth Sciences, University of Tabriz,


Qorveh-Dehgolan plain is the largest plain of Kurdistan province, which is the most active agricultural area in the province. In recent years, agricultural development has tended to increase the use of chemical fertilizers and has caused aquifers in the plain to be contaminated. Therefore, it is necessary to determine vulnerable areas for managing areas at risk. In this research, the aquifer vulnerability of Qorveh-Dehgolan plain has been investigated using DRASTIC, SINTACS and SI methods, which is one of the most common ranking methods for assessing aquifer vulnerability. Considering that each of these methods has its own advantages, these methods were combined using Sagnuo fuzzy model, genetic algorithm and combined method as an unsupervised and supervised combination method. The results showed that correlation index of all three combined methods is more than single methods (DRASTIC, SINTACS and SI). In combination methods, the method of Sagnuo fuzzy model combination has the highest correlation index, and the correlation coefficient of this method is higher than the other combination and single methods. Therefore, the combination method of Sagnuo fuzzy model is a better method for assessing the of Qorveh-Dehgolan aquifer than other methods. Based on this method, about 21.5%, 49.4%, 24.7% and 4.4% of the aquifer are in the low, medium, high and very high vulnerability, respectively. The north-west and south-east parts of the plain have more potential pollution than other parts of the plain, and more protection from these areas needs to be done.


Main Subjects

[1]. Stigter TY, Ribeiri L, Carvalho Dill AMM. Evaluation of an intrinsic and a specific Vulnerability assessment methodin comparsion with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeology journal. 2006; 14:79-99.
[2]. Amasri MN. Assssment of intrinsic Vulnerability to contamination for Gaza Coastal aquifer, palestin. Journal of Environmental management. 2008; 88:577-593.
[3]. Maarofi S, Soleymani S, Ghobadi M, Rahimi Gh, Maarofi H. Malayer aquifer vulnerability assessment using models DRASTIC, SI and SINTACS. Soil and Water Research Journal.2011; 19(3). [Persian]
[4]. Foster SS. Fundamental concepts in aquifer vulnerability, pollution ris and protection strategy. In: van Duijvenbooden, W, Van Waegeningh, HG (Eds.), Vulnerability of Soils and Groundwater to Pollution. TNO Committee of Hydrological Research, the Hague, Proceedings and information; 1987. 38: 69-86.
[5]. Aller L, Bennet T, Leher J, Petty R., Hackett G. DRASTIC: A Standardized system for evaluating groundwater pollution potential using hydro-geological settings, Kerr Environmental Research Laboratory. U.S Environmental Protection Agency Report. 1987; (EPA/600/2-87/035).
[6]. Van Stemproot D. Evert L, Wassenaar L. Aquifer vulnerability index: a GIS compatible method for groundwater vulnerability mapping. Canadian Water Resources Journal; 1994. 18: 25-37.
[7]. Civita M, Massimo. Legenda unificata per le Carte della vulnerabilita dei corpi idrici sotteranei/ unified legend for the aquifer pollution vulnerability Maps, Studi sulla Vulnerabilita degli Acquiferi, Pitagora Edir, Bologna; 1990.
[8]. Ribeiro L. Desenvolvimento de um I ‘ndice para avaliar a susceptibilidade, ERSHA-CVRM; 2000.
[9]. Asghari Moghaddam A, Fijani E, Nadiri AA. Groundwater Vulnerability Assesment Using GIS-Based DRASTIC Model in the Bazargan and Poldasht Plains. Journal of Environmental Studies. 2010; 35, 52.
[10]. AsghariMoghaddam A, Barzegar R. Investigation of Nitrate Concentration Anomaly Source and Vulnerability of Groundwater Resources of Tabriz Plain Using AVI and GOD Methods. Water and soil Science. 2015; 24(4): 11-27. [Persian].
[11]. Fakhri MS, AsghariMoghaddam A, Najib M Barzegar R. Investigation of nitrate cincentrations in groundwater resources of Marand plain and groundwater vulnerability assessment using AVI and GODS methods. Joirnal of Environmental Studies. 2015; 41(1): 49-66. [Persian].
[12]. Bai L,Wang Y, Meng F. Application of DRASTIC and extension theory in the groundwater vulnerability evaluation. Water and Environment journal. 2012; 26(3):381–391.
[13]. Huan H, Wang J, Teng Y. Assessment and validation of groundwater vulnerability to nitrate based on a modified DRASTIC model: A case study in Jilin City of northeast China. Sci Total Environ. 2012; 440:14–23.
[14]. Jafari SM, Nikoo MR. Groundwater risk assessment based on optimization framework using DRASTIC method. Arab J Geosci. 2016; 9:742.
[15]. Hamza, MH, Added A, France’s A, Rodri’guez R. Validite’ de l’application des me’thodes de vulne’rabilite’ DRASTIC, SINTACS, SI et a l’e’tude de la pollution par lesnitrates dans la nappe phre’atique de Metline Ras Jebel-Raf Raf, Compets Rendus Geoscience. 2007; 339:493-505.
[16]. Asghari Moghaddam A , Gharekhani M, Nadiri AA, Kord M, Fijani E. Evaluation of intrinsic vulnerability of Ardabil plain using DRASTIC, SINTACS and SI methods. Journal of geography and planning. 2017; 21 (61): 57-74.
[17]. Javanshir G, Nadiri AA, Sadeghfam S, Abbas novinpour A. Introducing a new method to aquifer vulnerability assessment of Moghan plain based on combination of DRASTIC, SINTACS and SI methods. Ecohydrology Journal. 2016: 496-467. [Persian]
 [18]. Nadiri AA, Gharekhani M, Khatibi R, Sadeghfam S, Asghari Moghaddam A. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Sci Total Environ. 2017; 574:691– 706.
[19]. Nadiri AA, Gharekhani M, Khatibi R, Asghari Moghaddam A. Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models. Environ Sci Pollut Res. 2017; 24(9):8562–8577.
[20]. Nadiri AA, Sedghi Z, Khatibi R, Gharekhani M. Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures. Sci Total Environ. 2017; 593-594:75–90.
[21]. Nadiri AA, Gharekhani M., Khatibi R. Mapping Aquifer Vulnerability Indices Using Artificial Intelligence-running Multiple Frameworks (AIMF) with Supervised and Unsupervised Learning. Journal of Water Resource Management. 2018; 32: 3023–3040.
[22]. Boughriba M, Barkaoui A, Zarhloule Y, Lahmer Z, El-Houadi B, Verdoya M. Groundwater vulnerability and risk mapping of the Angad transboundary aquifer using DRASTIC index method in GIS environment. Arabian Journal of Geoscience. 2009; 3:207-220.
[23]. 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. 2005; 12: 432-458.
[24]. Nadiri AA, Naderi K, Khatibi R, Gharekhani M. Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrological Sciences journal. 2019; 64: 210-226.
[25]. Piscopo G. Groundwater vulnerability map, explanatory notes, Castlreagh Catchment, NSW, Department of Land and Water Conservation, Australia; 2001.
[26]. Todd D K. Mays L W. Groundwater Hydrology. Third Ed., John Wiley & Sons Inc., U.S.A; 2005.
[27]. Fijani E, Nadiri AA, Asghari Moghadam A; Tsai F T-C, Dixon B. Optimaization of DRASTIC Method by Supervised Committww Machine Artificial Intelligence to Assess Groundwater Vulnerability for Maragheh-Bonab Plain Aquifer, Iran. Journal of hydrology. 2013; 530: 89-100.
[28]. Hongxing L, Chen P C P, Huang H P. Fuzzy Neural Intelligent System, Mathematical Foundation and the Application in Engineering, CRC Press LLC; 2001.
[29] Nadiri A, Sedghi Z, Evaluation of multiple aquifer vulnerability using DRASTIC, SINTACS methods. Journal of Hydrogeology, 2019, In press.
[30] Faal Aghdam R, Nadiri AA, Abbas Novinpour E. Evaluation of Bilverdi plain aquifer vulnerability based on combination of DRASTIC and SINTACS methods, 2018, 6(3): 135-150.
Volume 6, Issue 3
September 2019
Pages 821-836
  • Receive Date: 18 April 2019
  • Revise Date: 21 June 2019
  • Accept Date: 21 June 2019
  • First Publish Date: 23 September 2019