اصغری مقدم، الف.، 1389، اصول شناخت آبهای زیرزمینی. انتشارات دانشگاه تبریز. ص506.
درویشزاده، الف.، 1380، زمینشناسی ایران. مؤسسه انتشارات امیرکبیر، تهران. ص 434.
سازمان آب منطقهای استان آذربایجان شرقی، 1393، گزارش نهایی مطالعات آبهای زیرزمینی دشتهای استان آذربایجان شرقی در محیط زیست، GIS. مهندسین مشاور اول.
سامانی، س.، 1395، بررسی هیدروژئولوژی و عدم قطعیت مدل آب زیرزمینی آبخوان دشت عجبشیر، آذربایجان شرقی. رساله دکتری، دانشکده علوم طبیعی، دانشگاه تبریز.
سلطانیسیسی، گ، الف، جلالزاده، م، حقفارسی، ی، یوسفیراد، الف. 1384. سازمان زمینشناسی و اکتشافات معدنی ایران.
قراداغی، م.، کتابچی، ح.، محمدولی سامانی، ج.، 1400، آسیبپذیری آبخوان ساحلی لاهیجان-چابکسر با استفاده از ارزیابی مقایسهای سه شاخص GALDIT، SINTACS و AVI. هیدروژئولوژی. 6(2)، 120-109. doi:
10.22034/hydro.2022.12373
Aller, L., Bennett, T., Lehr, J., and Petty R., 1987. DRASTIC: a standardized system for evaluating groundwater pollution using hydrogeologic settings. US EPA, Robert S. Kerr Environmental Research Laboratory. 85(2). doi:
10.4236/eng.2016.811067.
Asghari Moghadam, A.,2010. Principles of Hydrogeology. University of Tabriz Publishing Institute. 506 P. (In Persian).
Babiker, I.S., Mohamed, M.A., Hiyama, T., and Kato, K.A., 2005. GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara, Heights, Gifu Prefecture, central Japan. Science of the Total Environment. 2005: 345:127–140.
Barzegar, R., Razzagh, S., Quilty, J., Adamowski, J., Kheyrollah Pour, H., and Booij, M.J., 2021. Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models. Journal of Hydrology. doi:
https://doi.org/10.1016/j.jhydrol.2021.126370.
Bordbar, M., Neshat, A., Javadi, S., Pradhan, B., and Aghamohammadi, H., 2020. Meta-heuristic algorithms in optimizing GALDIT framework: a comparative study for coastal aquifer vulnerability assessment. Journal of Hydrology. doi:
https://doi.org/10.1016/j.jhydrol.2020.124768.
Bordbar, M., Neshat, A., Javadi, S., and Shahdany, S.M.H., 2021. A Hybrid Approach Based on Statistical Method and Meta-heuristic Optimization Algorithm for Coastal Aquifer Vulnerability Assessment. Environmental Modelling & Assessment. 1-14. doi:
https://doi.org/10.1007/s10666-021-09754-w.
Bouderbala, A., Remini, B., Hamoudi, S., and Pulido-Bosch, A., 2016. Assessment of groundwater vulnerability and quality in coastal aquifers: a case study (Tipaza, North Algeria) Arab J Geosci. 9(181). doi:
10.1007/s12517-015-2151-6.
Calvo, P.I., and Estrada, G.J.C., 2009. Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosystems Engineering. 102:202–218. doi:
10.1016/j.biosystemseng.2008.09.032.
Chachadi, A.G., and Lobo Ferreira, J.P.C., 2001. Seawater intrusion vulnerability mapping of aquifers using the GALDIT method, Coastin-A Coastal Policy Res News. l (4):7–9. doi:
10.4236/jgis.2016.84044.
Chachadi, A.G., 2005. Seawater intrusion mapping using modified GALDIT indicator model- case study in Goa. Jalvigyan Sameek. 20:29–45. doi:
10.4236/gep.2017.53015.
Darvishzadeh, A., 2001. Geology of Iran. Amir Kabir Publishing Institute: Tehran. 434 P. (In Persian).
Debeljak, M., and Džeroski, S., 2011. Decision trees in ecological modelling. Modelling Complex Ecological Dynamics: An Introduction into Ecological Modelling for Students. Teachers & Scientists. 197-209. doi:
10.1007/978-3-642-05029-9_14.
Dong, Y., Zhou, W., Wang, X., Lu, Y., Zhao, P., and Li, X., 2020. A new assessment method for the vulnerability of confined water:WF &PNN method. Journal of Hydrology. 590:125217. doi:
10.1016/j.jhydrol.2020.125217.
East Azerbaijan Regional Water Co., 2014. The final report of groundwater detailed studies of the plains of East Azerbaijan Province in the environment, GIS. consulting engineers of the first. (In Persian).
Fakhri, M.S., Asghari
Moghaddam, A., Nadiri, A.A., Barzegar, R., and Cloutier, V., 2024. Incorporating Hydraulic Gradient and Pumping Rate into GALDIT Framework for Salinity Hazard Assessment in Coastal Aquifers: A Case Study of Urmia Plain, Iran. PREPRINT (Version 1) available at Research Square.
https://doi.org/10.21203/rs.3.rs-4186756/v1.
Foster, S.S.D., 1987. Fundamental concepts in aquifer vulnerability. pollution risk and protection strategy.
Gharadaghi, M., Ketabchi, H., and Mohammad-Vali-Samani, J., 2021. Vulnerability of Lahijan-Chaboksar aquifer using comparative assessment of three indices of GALDIT, SINTACS, and AVI. Hydrogeology. 6(2), 109-120. (In Persian).
Gharekhani, M., Nikoo, M. R., Nadiri, A. A., Al-Rawas, G., Sana, A., Gandomi, A. H., Nematollahi, B., and Senapathi, V., 2023. A new approach for assessing the assembled vulnerability of coastal aquifers based on optimization models. Journal of Hydrology, 625, 130084.
https://doi.org/10.1016/j.jclepro.2018.01.139.
Goldberg, D.E., 1989. Genetic algorithms in search, optimization and machine learning, 1st Ed. Addison-Wesley Publishing Company, New York.
Gorgij, A.D., and Asghari Moghaddam, A., 2016. Vulnerability assessment of saltwater intrusion using simplified GAPDIT method: a case study of Azarshahr Plain Aquifer, East Azerbaijan, Iran. Arab J Geosci. 9:106. doi:
https://doi.org/10.1007/s12517-015-2200-1.
Hosseini, F.S., Choubin, B., Mosavi, A., Nabipour, N., Shamshirband, S., Darabi, H., and Haghighi, A.T., 2020. Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. science of the total environment. 711:135161. doi:
10.1016/j.scitotenv.2019.135161.
Hu, X., Ma, C., Qi, H., and Guo, X., 2018. Groundwater vulnerability assessment using the GALDIT model and the improved DRASTIC model: a case in Weibei Plain, China. Environmental Science and Pollution Research. 25(32):32524-32539. doi:
10.1007/s11356-018-3196-3.
Kazakis, N., Spiliotis, M., Voudouris, K., Pliakas, F.K., and Papadopoulos, B., 2018. A fuzzy multicriteria categorization of the GALDIT method to assess seawater intrusion vulnerability of coastal aquifers. Sci. Total Environ. 621:524-534. doi:
10.1016/j.scitotenv.2017.11.235.
Mitchell, M., 1996. An Introduction to Genetic Algorithms. Massachusetts Institute of Technology. Cambridge.
Motevalli, A., Moradi, H.R., and Javadi, S., 2018. A Comprehensive evaluation of groundwater vulnerability to saltwater up coning and seawater intrusion in a coastal aquifer (case study: Ghaemshahr-juybar aquifer). Journal of Hydrology, 557, 753-773.
Nadiri, A.A., Gharekhani, M., Khatibi, R., and
Asghari Moghaddam, A., 2017b. Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models. Environ Sci Pollut Res. 24, 8562–8577.
https://doi.org/10.1007/s11356-017-8489-4.
Nadiri, A.A., Gharekhani, M., Khatibi, R., Sadeghfam, S., and Asghari Moghaddam, A., 2017a. Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Sci Total Environ, 1;574:691-706.
https://doi:10.1016/j.scitotenv.2016.09.093.
Nadiri, A.A., Naderi, K., Khatibi, R., and Gharekhani, M., 2019. Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrological Sciences Journal, 64(2), 210–226.
https://doi.org/10.1080/02626667.2018.1554940.
Nadiri, A.A., Sedghi, Z., Khatibi, R., and Sadeghfam, S., 2018. Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks. J Environ Manage. 1;227: 415-428. https://doi: 10.1016/j.jenvman.2018.08.019.
Samani, S., 2016. Hydrogeological study and uncertainty of the groundwater model of Ajab-Shir plain, East Azerbaijan. Ph.D. Thesis in Hydrogeology, Faculty of Natural Sciences, University of Tabriz. (In Persian).
Skurichina, M., and Duin, R.P., 2002. Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis and Applications. 5:121–135. doi: https://doi.org/10.1007/s100440200011.
Soltani Sisi, G.A., Jalalzadeh, M., Haghfarshi, E., and Yosefirad, A., 2005. Geological survey and mineral exploration of Iran. (In Persian).
Sugeno, M., 1985. Industrial applications of fuzzy control. Elsevier Science Inc. Amsterdam.
Tayfur, G., Nadiri, A.A., Asghari Moghadam, A., 2014. Supervised Intelligent Committee Machine method for hydraulic conductivity estimation. Water Resources Management. 28:1173–1184. doi:
10.1007/s11269-014-0553-y.
Wang, Y., and Witten, I.H., 1996. Induction of model trees for predicting continuous classes, (Working paper 96/23), Hamilton, New Zealand: University of Waikato. Department of Computer Science. doi:
https://digitalnz.org/records/432394.
Zadeh, L.A., 1965. Fuzzy Sets. Information Control. 8, 338-353. Doi:
http://dx.doi.org/10.1016/S0019-9958(65)90241-X.