بهینه سازی مدل DRASTIC جهت بررسی آسیب پذیری آبخوان قزوین با ابزار DA و GIS

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، پردیس ابوریحان، دانشگاه تهران، تهران، ایران.

2 نویسنده مسئول، گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، ایران.

10.22059/jwim.2023.355062.1052

چکیده

درسال­های اخیر برداشت از آب­زیرزمینی به‌ویژه در مناطق خشک و نیمه‌خشک با توجه به افزایش جمعیت، نیاز روزافزون به محصولات کشاورزی و نیز تقاضای صنعت به‌صورت چشمگیری افزایش یافته است. افزایش برداشت از آبخوان­ها مقارن با آلودگی و کاهش کیفیت آن‌ها نیز شده است. یکی از راه‌کارهای مؤثر در حفاظت از این منابع، شناسایی مناطق دارای پتانسیل بالای آسیب­پذیری می­باشد. پژوهش‌گران روش­های زیادی را برای ارزیابی آلودگی و پتانسیل آسیب­پذیری منابع آب زیرزمینی ارائه نموده­اند که بیش‌تر آن‌ها براساس روش شاخص DRASTIC بنا شده­اند. هم‌چنین در سال‌های اخیر پژوهش‌گران بسیاری نیز جهت بهبود شاخص اقدام به اصلاح آن نمودند. از این‌رو، در این پژوهش وزن پارامترهای شاخص DRASTIC با استفاده از دو روش آماری رگرسیون لجستیک و تحلیل تشخیصی ارتقا داده شده است. به‌منظور صحت­سنجی مدل­های DRASTIC-DA و DRASTICQ-Log از همبستگی این دو شاخص و غلظت نیترات در دشت قزوین استفاده گردید. نتایج پژوهش نشان داد، ضریب همبستگی بین غلظت نیترات و شاخص آسیب­پذیری در مدل DRASTIC، DRASTIC-Log، DRASTIC-DA1 و DRASTIC-DA2 به‌ترتیب برابر 40، 4/48، 8/51 و 5/55 درصد می­باشد که این موضوع نشان‌دهنده این مطلب است که در تعیین وزن ضرایب روش DRASTIC، روش DRASTIC-Log از دقت بالاتری نسبت به روش DRASTIC برخوردار می‌باشد و هم‌چنین استفاده از روش تحلیل تشخیصی رویکرد مناسب­تری نسبت به روش رگرسیون لجستیک خواهد داشت.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Optimizing DRASTIC Index to Assess The Vulnerability of Qazvin Aquifer with DA And GIS Tools

نویسندگان [English]

  • Samaneh Ghafoori-Kharanagh 1
  • Nargeskhatoon dowlatabadi 1
  • Aminreza Neshat 2
1 Department of Water Engineering, College of Abouraihan, University of Tehran, Tehran, Iran.
2 Department of GIS/RS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

In recent years, the extraction of groundwater, especially in arid and semi-arid areas, has increased significantly due to the increase in population, the growing need for agricultural products, and the demand of industry. The increase in extraction from aquifers has been paralleled by the pollution and decrease in their quality. One of the effective ways to protect these resources is to identify areas with high vulnerability potential. Researchers have provided many methods to evaluate the pollution and vulnerability potential of groundwater sources, most of them are based on DRASTIC index. Also, in recent years, many researchers have modified it to improve the index. Therefore, in this research, the weight of DRASTIC index parameters has been improved using two statistical methods, logistic regression, and Discriminant Analysis. To validate DRASTIC-DA and DRASTIC-Log models, the correlation between these two indicators and nitrate concentration in Qazvin plain was used. The research results showed that the correlation coefficient between nitrate concentration and vulnerability index in the DRASTIC, DRASTIC-Log, DRASTIC-DA1 and DRASTIC-DA2 models are 40, 48.4, 51.8 and 55.5 percent, respectively. This is shows that the DRASTIC-Log method is more accurate than the DRASTIC method in determining the weight of the coefficients of the DRASTIC index, and the use of the Discriminant Analysis method will have a more appropriate approach than the Logistic Regression method.

کلیدواژه‌ها [English]

  • Groundwater vulnerability
  • logistic regression (Log)
  • discriminant analysis (DA)
  • GIS

مراجع

  1. Aller, L., Bennet, T., Lehr, J.H., Petty, R.J., & Hackett, G. (1987). DRASTIC: a standardized system for evaluating groundwater pollution  potential  using  hydrogeological  settings.  EPA/600/2–87/035.  US Environmental Protection Agency, Ada, OK, USA.
  2. Antonogeorgos, G., Panagiotakos, D.B., Priftis, K.N., & Tzonou, A. (2009). Logistic regression and linear discriminant analyses in evaluating factors associated with asthma prevalence among 10-to 12-years-old children: Divergence and similarity of the two statistical methods. International journal of pediatrics, 2009.
  3. Celico, F., & Naclerio, G. (2005). Verification of a DRASTIC-based method for limestone aquifers. Water international, 30(4), 530-537.
  4. Chitsazan, M., & Akhtari, Y. (2009). A GIS-based DRASTIC model for assessing aquifer vulnerability in Kherran Plain, Khuzestan, Iran. Water resources management, 23(6), 1137-1155.
  5. Dixon, B. (2004). Prediction of groundwater vulnerability using an integrated GIS-based neuro-fuzzy techniques. Journal of Spatial Hydrology, 4 (2), 1-14.
  6. Fritch, T.G., Mcknight, C.L., Yelderman Jr, J.C., & Arnold, J.G. (2000). An aquifer vulnerability assessment of the Paluxy aquifer, central Texas, USA, using GIS and a modified DRASTIC approach. Environmental management, 25(3), 337-345.
  7. Jain, A.K., & Jha, C.K. (2017). Dropout Classification through Discriminant Function Analysis: A Statistical Approach.
  8. Jang, C.S., Lin, C.W., Liang, C.P., & Chen, J.S. (2016). Developing a reliable model for aquifer vulnerability. Stochastic environmental research and risk assessment, 30(1), 175-187.
  9. Javadi, S., Hashemy, S.M., Mohammadi, K., Howard, K.W.F., & Neshat, A. (2017). Classification of aquifer vulnerability using K-means cluster analysis. Journal of hydrology, 549, 27-37.
  10. Javadi, S., Kavehkar, N., Mohammadi, K., Khodadadi, A., & Kahawita, R. (2011). Calibrating DRASTIC using field measurements, sensitivity analysis and statistical methods to assess groundwater vulnerability. Water International, 36(6), 719-732.
  11. Javadi, S., Kavehkar, N., Mousavizadeh, M.H., & Mohammadi, K. (2010). Modification of DRASTIC model to map groundwater vulnerability to pollution using nitrate measurements in agricultural areas. Journal of Agricultural Science and Technology, 13, 239-249.
  12. Jmal, I., Ayed, B., Boughariou, E., Allouche, N., Saidi, S., Hamdi, M., & Bouri, S. (2017). Assessing groundwater vulnerability to nitrate pollution using statistical approaches: a case study of Sidi Bouzid shallow aquifer, Central Tunisia.Arabian Journal of Geosciences, 10(16), 364.
  13. Kholghi, M., Hassanzadeh, H., & Keyvanpour, M. (2010, May). Classification and evaluation of data mining techniques for data stream requirements. In Computer Communication Control and Automation (3CA), 2010 International Symposium on (Vol. 1, pp. 474-478). IEEE.
  14. Kosaki, T., Wasano, K., & Juo, A.S. (1989). Multivariate statistical analysis of yield-determining factors. Soil Science and Plant Nutrition, 35(4), 597-607.
  15. Lee,   (2005).  Application  of  logistic  regression  model  and  its  validation  for  landslide  susceptibility mapping using GIS and remote sensing data. Journal of Remote Sensing, 26, 1477-1491.
  16. Li, R., Merchant, J. W., & Chen, X. H. (2014). A geospatial approach for assessing groundwater vulnerability to nitrate contamination in agricultural settings. Water, Air, & Soil Pollution, 225(12), 2214.
  17. Liang, C.P., Jang, C.S., Liang, C.W., & Chen, J.S. (2016). Groundwater Vulnerability Assessment of the Pingtung Plain in Southern Taiwan. International journal of environmental research and public health, 13(11), 1167.
  18. Magyar, N., Kovacs, J., Tanos, P., Trasy, B., Garamhegyi, T., & Hatvani, I. G. (2017). Application of Combined Cluster and Discriminant Analysis to Make the Operation of Monitoring Networks More Economical. World Academy of Science, Engineering and Technology, International Journal of Marine and Environmental Sciences, 4(5).
  19. Mair, A., & El-Kadi, A.I. (2013). Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA. Journal of contaminant hydrology, 153, 1-23.
  20. Mishra, D., Chakrabarty, R., Sen, K., Pal, S.C., & Mondal, N.K. (2023). Groundwater vulnerability assessment of elevated arsenic in Gangetic plain of West Bengal, India; Using primary information, lithological transport, state-of-the-art approaches. Journal of Contaminant Hydrology, 104195.
  21. Mohammadi, K., Niknam, R., & Majd, V.J. (2009). Aquifer vulnerability assessment using GIS and fuzzy system: a case study in Tehran–Karaj aquifer, Iran. Environmental Geology, 58(2), 437-446.
  22. Neshat, A., Pradhan, B., Pirasteh, S., & Shafri, H.Z.M. (2014). Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran. Environmental Earth Sciences, 71(7), 3119-3131.
  23. Nolan, B. T., Hitt, K. J., & Ruddy, B. C. (2002). Probability of nitrate contamination of recently recharged groundwaters in the conterminous United States. Environmental science & technology, 36(10), 2138-2145.
  24. Poulsen, J., & French, A. (2008). Discriminant function analysis. Retrieved from.
  25. Shirzadi, ,  Saro, L., Hyun-Joo, Oh., & Chapi, K. (2012).  A  GIS-based  logistic  regression  model  in  rock  fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Natural Hazard, 64, 1639-1656.
  26. Sinan, M., & Razack, M. (2009). An extension to the DRASTIC model to assess groundwater vulnerability to pollution: application to the Haouz aquifer of Marrakech (Morocco). Environmental Geology, 57(2), 349-363.
  27. Tabachnick, B.G., & Fidell, L.S. (1996). Using Multivariate Statistics. Harper Collins College Publishers: New York. Tabachnick and Fidell compare and contrast statistical packages, and can be used with a modicum of pain to understand SPSS result print-outs.
  28. Torkashvand, M., Neshat, A., Javadi, S., & Pradhan, B. (2021). New hybrid evolutionary algorithm for optimizing index-based groundwater vulnerability assessment method. Journal of Hydrology, 598, 126446.
مدیریت آب و آبیاری
دوره 13، شماره 2
تیر 1402
صفحه 565-580

فایل ها

هم رسانی

ارجاع به این مقاله

آمار