ارزیابی آسیب پذیری آب های زیرزمینی در برابر آلودگی بر اساس روش های جدید ترکیبی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه سنجش از دورو سیستم اطلاعات جغرافیایی، دانشکده منابع طبیعی و محیط زیست، واحد علوم وتحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 گروه علوم محیط زیست و جنگل، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

10.22059/jwim.2024.373115.1150

چکیده

مسئله مهم در مورد منابع آب زیرزمینی، آگاهی از میزان آلودگی سفره آب زیرزمینی است که  منجر به مدیریت مناطق مستعد آلودگی می‌شود. ارزیابی آسیب‌پذیریآب‌های زیرزمینی می‌تواند نقشی حیاتی در حفاظت، بهره‌برداری و اولویت‌بندی مناطق جهت کنترل و استفاده از طرح‌های پیشگیرانه ایفا نماید. با توجه به ماهیت منطقه، فعالیت‌های کشاورزی و افزایش غلظت نیترات، روش DRASTIC نیاز به اصلاح دارد. هدف پژوهش حاضر اصلاح وزن‌های اولیه مدلDRASTIC  است که با درنظرگرفتن اهمیت اصلاح رتبه‌بندی و تأثیر روش‌های وزن‌دهی در آبخوان یاسوج است. از چارچوب نسبت فرکانس برای کالیبره‌کردن نرخ‌های شاخص DRASTIC استفاده شد. در ادامه اصلاح وزن پارامترهای DRASTIC  در دو مرحله پژوهش، که مرحله اول شامل روش‌های آنتروپی شانون و SPSA و مرحله دوم شامل روش‌های (BWM) بهترین- بدترین و تحلیل نسبت ارزیابی وزن‌دهی تدریجی (SWARA) است، انجام شد. بنابراین، نُه چارچوب شامل FR_DRASTIC، DRASTIC_Entropy، DRASTIC_SPSA، DRASTIC_SWARA، DRASTIC_BWM، FR_Entropy، FR_SPSA، FR_BWM، FR_SWARA به‌دست آمد. از غلظت نیترات  نمونه چاه‌ها برای اعتبارسنجی شاخص‌های آسیب‌پذیری استفاده شد. اعتبارسنجی با روشROC Curve  انجام شد. FR_SWARA با سطح زیر منحنی 80/0 عملکرد بهتری نسبت به سایر روش‌ها داشت.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Assessment of groundwater vulnerability to pollution based on new hybrid approach methods

نویسندگان [English]

  • Aminreza Neshat 1
  • Masoumeh Abed 1
  • Mahdi Ramezani 2
1 Department of GIS/RS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Department of Environmental and Forest Sciences, Faculty of Natural Resource and Environment, Science and Research Branch,Islamic Azad University, Tehran, Iran.
چکیده [English]

The important issue regarding underground water resources is to know the extent of groundwater pollution, which leads to the management of areas prone to pollution. Groundwater vulnerability assessment can play a vital role in protecting, exploiting and prioritizing areas for controlling and using preventive plans. Due to the nature of the area, agricultural activities and nitrate increase, the DRASTIC method needs to be modified. The aim of the current research is to modify the weights of the DRASTIC model, which is considering the importance of modifying the ranking and the effect of weighting methods in the Yasouj aquifer.The frequency ratio framework was used to calibrate the DRASTIC index rates. Then, the weight correction of DRASTIC parameters was done in two stages of research, the first stage includes Shannon entropy and SPSA methods and the second stage includes BWM (Best Worst Method) and SWARA (Stepwise Weight Assessment Ratio Analysis) methods. Therefore, nine frames including FR_DRASTIC, DRASTIC_Entropy, DRASTIC_SPSA, DRASTIC_SWARA, DRASTIC_BWM, FR_Entropy, FR_SPSA, FR_BWM, FR_SWARA were obtained. The nitrate concentration of the well samples was used to validate the vulnerability indicators. Validation was done by ROC Curve method. FR_SWARA performed better than other methods with the area under the curve of 0.80.

کلیدواژه‌ها [English]

  • BWM
  • Entropy
  • Nitrate
  • SWARA
  1. Ahmad, W. N. K. W., Rezaei, J., Sadaghiani, S., & Tavasszy, L. A. (2017). Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. Journal of Cleaner Production, 153, 242-252.
  2. Ahmed, A. A. (2009). Using generic and pesticide DRASTIC GIS-based models for vulnerability assessment of the Quaternary aquifer at Sohag, Egypt. Hydrogeology Journal, 17(5), 1203-1217.
  3. Aller, L., Lehr, J., & Petty, R. (1987). DRASTIC: a standardized system to evaluate ground water pollution potential using hydrogeologic settings. National water well Association Worthington, Ohio 43085. Truman Bennett. Bennett and Williams. Columbus, Ohio, 43229.
  4. Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7), 1145-1159.
  5. Brindha, K., & Elango, L. (2015). Cross comparison of five popular groundwater pollution vulnerability index approaches. Journal of hydrology, 524, 597-613.
  6. Bordbar, M., Neshat, A., Javadi, S., & Shahdany, S. M. H. (2021). A hybrid approach based on statistical method and meta-heuristic optimization algorithm for coastal aquifer vulnerability assessment. Environmental Modeling & Assessment26, 325-338.
  7. Busico, G., Kazakis, N., Cuoco, E., Colombani, N., Tedesco, D., Voudouris, K., & Mastrocicco, M. (2020). A novel hybrid method of specific vulnerability to anthropogenic pollution using multivariate statistical and regression analyses. Water Research, 171, 115386.
  8. Chen, W., Pourghasemi, H. R., Kornejady, A., & Zhang, N. (2017). Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma, 305, 314-327.
  9. Chukwuma, E. C., Okonkwo, C. C., Afolabi, O. O. D., Pham, Q. B., Anizoba, D. C., & Okpala, C. D. (2023). Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model. Environmental Science and Pollution Research, 30(17), 49856-49874.
  10. Expósito, J. L., Esteller, M. V., Paredes, J., Rico, C., & Franco, R. (2010). Groundwater protection using vulnerability maps and wellhead protection area (WHPA): a case study in Mexico. Water resources management, 24, 4219-4236.
  11. Gorelick, S. M., & Zheng, C. (2015). Global change and the groundwater management challenge. Water Resources Research, 51(5), 3031-3051.
  12. Hao, J., Zhang, Y., Jia, Y., Wang, H., Niu, C., Gan, Y., & Gong, Y. (2017). Assessing groundwater vulnerability and its inconsistency with groundwater quality, based on a modified DRASTIC model: a case study in Chaoyang District of Beijing City. Arabian Journal of Geosciences, 10, 1-16.
  13. Hashemkhani Zolfani, S., Yazdani, M., & Zavadskas, E. K. (2018). An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Computing, 22, 7399-7405.
  14. Iqbal, J., Gorai, A., Katpatal, Y., & Pathak, G. (2015). Development of GIS-based fuzzy pattern recognition model (modified DRASTIC model) for groundwater vulnerability to pollution assessment. International journal of environmental science and technology, 12, 3161-3174.
  15. Islam, A. R. M. T., Talukdar, S., Mahato, S., Kundu, S., Eibek, K. U., Pham, Q. B., ..., Linh, N. T. T. (2021). Flood susceptibility modelling using advanced ensemble machine learning models. Geoscience Frontiers, 12(3), 101075.
  16. Jenks, G. F. (1977). Optimal data classification for choropleth maps. Department of Geographiy, University of Kansas Occasional Paper.
  17. Jhariya, D. (2019). Assessment of groundwater pollution vulnerability using GIS-based DRASTIC model and its validation using nitrate concentration in Tandula Watershed, Chhattisgarh. Journal of the Geological Society of India, 93, 567-573.
  18. Kumar, P., Bansod, B. K., Debnath, S. , Thakur, P. K., & Ghanshyam, C. (2015). Index-based groundwater vulnerability mapping models using hydrogeological settings: a critical evaluation. Environmental Impact Assessment Review, 51, 38-49.
  19. Li, P., Karunanidhi, D., Subramani, T., & Srinivasamoorthy, K. (2021). Sources and consequences of groundwater contamination. Archives of environmental contamination and toxicology, 80, 1-10.
  20. Liu, M., Xiao, C., & Liang, X. (2022). Assessment of groundwater vulnerability based on the modified DRASTIC model: a case study in Baicheng City, China. Environmental earth sciences, 81(8), 230.
  21. Manap, M. A., Nampak, H., Pradhan, B., Lee, S., Sulaiman, W. N. A., & Ramli, M. F. (2014). Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arabian Journal of Geosciences, 7, 711-724.
  22. Neshat, A., & Pradhan, B. (2015). An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment. Natural Hazards, 76, 543-563.
  23. 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, 3119-3131.
  24. Neshat, A., Pradhan, B., & Dadras, M. (2014). Groundwater vulnerability assessment using an improved DRASTIC method in GIS. Resources, Conservation and Recycling86, 74-86.
  25. Obuchowski, N. A., & Bullen, J. A. (2018). Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine. Physics in Medicine & Biology, 63(7), 07TR01.
  26. Paryani, S., Neshat, A., & Pradhan, B. (2021). Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches. Theoretical and Applied Climatology146(1), 489-509.
  27. Paryani, S., Neshat, A., Pourghasemi, H. R., Ntona, M. M., & Kazakis, N. (2022). A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping. Science of The Total Environment807, 151055.
  28. Pouyan, S., Pourghasemi, H. R., Bordbar, M., Rahmanian, S., & Clague, J. J. (2021). A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Scientific Reports, 11(1), 14889.
  29. Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747-759.
  30. Rezaei, F., Safavi, H. R., & Ahmadi, A. (2013). Groundwater vulnerability assessment using fuzzy logic: a case study in the Zayandehrood aquifers, Iran. Environmental management, 51, 267-277.
  31. Shakeri, R., Alijani, F., & Nassery, H. R. (2023). Comparison of the DRASTIC+ L and modified VABHAT models in vulnerability assessment of Karaj aquifer, central Iran, using MCDM, SWARA, and BWM methods. Environmental earth sciences, 82(4), 97.
  32. Shrestha, A., & Luo, W. (2018). Assessment of groundwater nitrate pollution potential in Central Valley Aquifer using geodetector-based frequency ratio (GFR) and optimized-DRASTIC methods. ISPRS international journal of geo-information, 7(6), 211.
  33. Torkashvand, M., Neshat, A., Javadi, S., & Yousefi, H. (2021). DRASTIC framework improvement using stepwise weight assessment ratio analysis (SWARA) and combination of genetic algorithm and entropy. Environmental Science and Pollution Research, 28, 46704-46724.
  34. Torkashvand, M., Neshat, A., Javadi, S., Yousefi, H., & Berndtsson, R. (2023). Groundwater vulnerability to nitrate contamination from fertilizers using modified DRASTIC frameworks. Water, 15(17), 3134.
  35. Yu, C., Zhang, B., Yao, Y., Meng, F., & Zheng, C. (2012). A field demonstration of the entropy-weighted fuzzy DRASTIC method for groundwater vulnerability assessment. Hydrological Sciences Journal, 57(7), 1420-1432.