روش جدید مبتنی بر اعداد Z برای ارزیابی آسیب‌پذیری ویژه آب‌های زیرزمینی مطالعه موردی: دشت های اردبیل و قروه-دهگلان

Akbarian Saravi, N., Yazdanparast, R., Momeni, O., Heydarian, D., Jolai, F., 2018. Location optimization of agricultural residues-based biomass plant using Z-number DEA. J. Ind. Syst. Eng. 12 (1), 39–65.

Aliev, R.A., Huseynov, O.H., Aliyev, R.R., Alizadeh, A.A., 2015. The Arithmetic of Z-Numbers: Theory and Applications. World Scientific, Singapore.

Aliev, R.A., Pedrycz, W., Huseynov, O.H., Eyupoglu, S.Z., 2016. Approximate reasoning on a basis of Z-number-valued if–then rules. IEEE Trans. Fuzzy Syst. 25 (6),1589–1600.

Aller, L., 1985. DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeologic settings. Robert S. Kerr Environmental Research Laboratory. Office of Research and Development, US Environmental Protection Agency.

Almasri, M.N., 2008. Assessment of intrinsic vulnerability to contamination for gaza coastal aquifer Palestine. J. Environ. Manag. 88 (4), 577–593.

Antonakos, A.K., Lambrakis, N.J., 2007. Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates based on the drastic model, an example from NE Korinthia, Greece. J. Hydrol. 333 (2–4), 288–304.

Baalousha, H.M., Tawabini, B., Seers, T.D., 2021. Fuzzy or non-fuzzy? a comparison between fuzzy logic-based vulnerability mapping and drastic approach using a numerical model a case study from Qatar. Water 13 (9), 1288.

Bayatikhatibi, M., Rostami, F., Valizadeh Kamran, K., 2022. Investigation and Assessment of Groundwater Vulnerability to Pollution using DRASTIC Model and Fuzzy Logic. Hydrogeomorphology. 8(29), 108-87.

Chakraborty, B., Roy, S., Bera, A., Adhikary, P.P., Bera, B., Sengupta, D., Bhunia, G.S., Shit, P.K., 2022. Groundwater vulnerability assessment using GIS-based DRASTIC model in the upper catchment of Dwarakeshwar river basin West Bengal, India. Environ. Earth Sci. 81 (1), 1–15.

Dhanya, C.T., Kumar, D.N., 2009. Data mining for evolving fuzzy association rules for predicting monsoon rainfall of India. J. Intell. Syst. 18 (3), 193–210.

Duhalde, D.J., Arumí, J.L., Oyarzún, R.A., Rivera, D.A., 2018. Fuzzy-based assessment of groundwater intrinsic vulnerability of a volcanic aquifer in the Chilean Andean Valley. Environ. Monit. Assess. 190 (7), 1–14.

Feizizadeh, B., Abdollahi, Z., & Shokati, B., 2022. A GIS-based spatiotemporal impact assessment of droughts in the hyper-saline Urmia Lake Basin on the hydro-geochemical quality of nearby aquifers. Remote Sensing, 14(11), 2516.

Ghosh, R., Sutradhar, S., Mondal, P., Das, N., 2021. Application of DRASTIC model for assessing groundwater vulnerability: a study on Birbhum district West Bengal, India. Model. Earth Syst. Environ. 7 (2), 1225–1239.

Glukhoded, E.A., Smetanin, S.I., 2016. The method of converting an expert opinion to Z-number. Proc. Inst. Syst. Program. RAS 28 (3), 7–20.

Gogu, R.C., Dassargues, A., 2000. Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environ. Geol. 39 (6), 549–559.

Gutiérrez-Estrada, J.C., Pedro-Sanz, E.de., López-Luque, R., Pulido-Calvo, I., 2004. Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla anguilla L.) intensive rearing system. Aquac. Eng. 31 (3–4), 183–203.

Han, J., Kamber, M., Pei, J., 2011. Data mining: concepts and techniques, Third. ed. Morgan Kaufmann, San Francisco.

Kadkhodaie, I.F., Asghari, M.A., Barzegar, R., Gharehkhani, M., 2020. Comparison of neural network and neuro-fuzzy techniques to improve the drastic frame work (Case Study: Shabestar plain Aquifer).

Kóczy, L.T., Hirota, K., 1991. Rule interpolation by 𝛼-level sets in fuzzy approximate reasoning. J. Busefal, Automne. URA-CNRS 46. 115–123.

Mamdani, E.H., 1977. Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Comput. 26 (12), 1182–1191.

Mehr, A.D., Nourani, V., Hrnjica, B., Molajou, A., 2017. A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events. J. Hydrol. 555, 397–406.

Najafi, H., Nourani, V., Sharghi, E., Roushangar, K., Dąbrowska, D., 2022. Application of Z-numbers to teleconnection modeling between monthly precipitation and large-scale sea surface temperature. Hydrol. Res. 53 (1), 1–13.

Nourani, V., Maleki, S., Najafi, H., Baghanam, A. H., 2023. A fuzzy logic-based approach for groundwater vulnerability assessment. Environmental Science and Pollution Research, 1-20.

Nourani, V., Najafi, H., Maleki, S., Paknezad, N. J., Huang, J. J., Zhang, P., Mohammadisepasi, S., 2024. Z-number based assessment of groundwater vulnerability to seawater intrusion. Journal of Hydrology. 130859.

Nourani, V., Najafi, H., Sharghi, E., Roushangar, K., 2021. Application of z-numbers to monitor drought using large-scale oceanic–atmospheric parameters. J. Hydrol. 598, 126198.

Novinpour, E.A., Moghimi, H., Kaki, M., 2022. Aquifer vulnerability based on classical methods and GIS-based fuzzy optimization method (case study: Chahardoli plain in kurdistan province iran). Arabian J. Geosci. 15 (4), 1–15.

Patel, P., Mehta, D., Sharma, N., 2022. A review on the application of the DRASTIC method in the assessment of groundwater vulnerability. Water Supply 22 (5), 5190–5205.

Piscopo, G., 2001. Groundwater vulnerability map explanatory notes—Castlereagh Catchment. NSW department of land and water conservation, Australia.

Shakoor, A., Khan, Z.M., Farid, H.U., Sultan, M., Ahmad, I., Ahmad, N., Mahmood, M.H., Ali, M.U., 2020. Delineation of regional groundwater vulnerability using DRASTIC model for agricultural application in Pakistan. Arab. J. Geosci. 13 (4), 1–12.

Sharghi, E., Paknezhad, N.J., Najafi, H., 2021. Assessing the effect of emotional unit of emotional ANN (EANN) in estimation of the prediction intervals of suspended sediment load modeling. Earth Sci. Inform. 14 (1), 201–213.

Stigter, T.Y., Ribeiro, L., Dill, A.M.M., 2006. Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeol. J. 14 (1), 79–99.

Takagi, T., Sugeno, M., 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Systems, Man Cybern. (1), 116–132.

Yu, H., Wu, Q., Zeng, Y., Zheng, L., Xu, L., Liu, S., Wang, D., 2022. Integrated variable weight model and improved drastic model for groundwater vulnerability assessment in a shallow porous aquifer. Journal of Hydrology 608, 127538.

Zadeh, L.A., 1965. Electrical engineering at the crossroads. IEEE Trans. Educ. 8 (2), 30–33.

Zadeh, L.A., 1973. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on systems, Man, and Cybernetics. 28–44.

Zadeh, L.A., 2011. A note on Z-numbers. Inform. Sci. 181 (14), 2923–2932.

Ziaye Shendershami, S., Esmali Ouri, A., Mostafazadeh, R., Ghorbani, A., 2021. Effective Factors in Ground Water Variations and Water Table Decrease in Ardabil Plain. Hydrogeomorphology. 8(28), 127-143.