مقایسۀ توانایی روش‏های مختلف ترکیبی در ارزیابی آسیب‏پذیری آب‏های زیرزمینی در آبخوان دشت قروه - دهگلان

نوع مقاله : پژوهشی

نویسندگان

1 دانشیار، دانشکده علوم طبیعی دانشگاه تبریز

2 دانشجوی کارشناسی ارشد هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز

3 دانشجوی دکتری هیدروژئولوژی، دانشکدۀ علوم طبیعی، دانشگاه تبریز

چکیده

دشت قروه- دهگلان بزرگ‏ترین دشت استان کردستان است که از نظر کشاورزی اهمیت زیادی برای این استان دارد. در سال‏های اخیر با توسعۀ کشاورزی، تمایل به استفاده از کودهای شیمیایی افزایش یافته و آبخوان‏های این دشت را در معرض آلودگی قرار داده است. بنابراین، تعیین مناطق آسیب‏پذیر برای مدیریت نواحی در معرض خطر، امری ضروری است. در تحقیق حاضر آسیب‏پذیری آبخوان دشت قروه- دهگلان با استفاده از روش‏های DRASTIC، SINTACS و SI ‌بررسی شده است. برای دست‌یابی به نتایج بهتر، روش‌های منفرد یادشده با روش‏های ترکیبی نظارت‌شده شامل مدل فازی ساجنو، الگوریتم ژنتیک و نیز روش‌های ‌نظارت‌نشده شامل روش مربوط به شاخص همبستگی (CI) ترکیب شدند. نتایج نشان داد شاخص همبستگی هر سه روش ترکیبی بیشتر از روش‏های منفرد (DRASTIC، SINTACS و SI) است و بین روش‏های ترکیبی، مدل فازی ساجنو بیشترین شاخص همبستگی را دارد و همچنین ضریب همبستگی این روش از باقی روش‏های ترکیبی و منفرد بیشتر است. مدل فازی ساجنو توانست 37 درصد ضریب همبستگی و 7 درصد شاخص همبستگی را نسبت به نتایج بهترین مدل منفرد افزایش دهد. بنابراین، روش ترکیبی نظارت‌شدۀ مدل فازی ساجنو برای ارزیابی آسیب‏پذیری آبخوان دشت قروه-دهگلان نسبت به بقیه روش‏ها بهتر است‌. براساس مدل فازی ساجنو، بخش‏های شمال‏ غرب و جنوب‏ شرق دشت پتانسیل آلودگی بیشتری نسبت به سایر مناطق آنجا دارند و باید محافظت بیشتری از این مناطق صورت بگیرد. بنابراین، می‌توان از روش ترکیبی نظارت‌شدۀ مدل فازی ساجنو برای بررسی آسیب‌پذیری آبخوان‏های دیگر نیز استفاده کرد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

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

نویسندگان [English]

  • Ata Allah Nadiri 1
  • Nasser Jabraili 2
  • Maryam Gharekhani 3
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,
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Groundwater vulnerability
  • Sagnuo fuzzy
  • Qorveh-Dehgolan plain
[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.