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Al-Abadi, A. M., A. A. Al-Temmeme, and M. A. Al-Ghanimy, 2016. A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra-Al Al-Gharbi-Teeb areas, Iraq, Sustainable Water Resources Management, 2(3): 265-283.
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Biswas, S., B. P. Mukhopadhyay, and A. Bera, 2020. Delineating groundwater potential zones of agriculture dominated landscapes using GIS based AHP techniques: a case study from Uttar Dinajpur district, West Bengal, Environmental Earth Sciences, 79(12): 302.
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Chen, W., W. Chen, M. Panahi, K. Khosravi, H. R. Pourghasemi, F. Rezaie, and D. Parvinnezhad, 2019c. Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization, Journal of Hydrology, 572: 435-448.
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Chen, W., B. Pradhan, S. Li, H. Shahabi, H. M. Rizeei, E. Hou, and S. Wang, 2019a. Novel Hybrid Integration Approach of Bagging-Based Fisher's Linear Discriminant Function for Groundwater Potential Analysis, Natural Resources Research, 28(4): 1239-1258.
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Zabihi, M., H. R. Pourghasemi, Z. S. Pourtaghi, and M. Behzadfar, 2016. GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran, Environmental Earth Sciences, 75(8): 665.
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Mallick, J., R. A. Khan, M. Ahmed, S. Alqadhi, M. Alsubih, I. Falqi, and M. A. Hasan, 2019. Modeling Groundwater Potential Zone in a Semi-Arid Region of Aseer Using Fuzzy-AHP and Geoinformation Techniques, Water, 11(12): 2656.
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Martinez-Santos, P. and P. Renard, 2019. Mapping Groundwater Potential Through an Ensemble of Big Data Methods, Groundwater, 58(4): 583-597.
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Falah, F., S. Ghorbani Nejad, O. Rahmati, M. Daneshfar, and H. Zeinivand, 2017. Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods, Geocarto International, 32(10): 1069-1089.
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Ghorbani Nejad, S., F. Falah, M. Daneshfar, A. Haghizadeh, and O. Rahmati, 2017. Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models, Geocarto International, 21: 167-187.
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Golkarian, A. and O. Rahmati, 2018. Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran, Environmental Earth Sciences, 77(10): 369-369.
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Golkarian, A., S. A. Naghibi, B. Kalantar, and B. Pradhan, 2018. Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS, Environmental Monitoring and Assessment, 190(3): 149.
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Kalantar, B., H. A. Al-Najjar, B. Pradhan, V. Saeidi, A. A. Halin, N. Ueda, and S. A. Naghibi, 2019. Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping, Water, 11(9): 1909.
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Mogaji, K. A., H. S. Lim, and K. Abdullah, 2015. Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster-Shafer model, Arabian Journal of Geosciences, 8(5): 3235-3258.
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Mousavi, S. M., A. Golkarian, S. A. Naghibi, B. Kalantar, and B. Pradhan, 2016. GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran, Geoscienses, 3(1): 91-115.
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Naghibi, S. A. and H. R. Pourghasemi, 2015. A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping, Water Resources Management, 29(14): 5217-5236.
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Naghibi, S. A. and M. Moradi Dashtpagerdi, 2016. Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features, Hydrogeology Journal, 25(1): 169-189.
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Karimi, V., R. Khatibi, M. Ghorbani, D. Tien Bui, and S. Darbandi, 2020. Strategies for Learning Groundwater Potential Modelling Indices under Sparse Data with Supervised and Unsupervised Techniques, Water Resources Management, 34(8): 2389-2417.
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Naghibi, S. A., H. R. Pourghasemi, and K. Abbaspour, 2018. A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS, Theoretical and Applied Climatology, 131(3): 967-984.
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Naghibi, S. A., H. Hashemi, R. Berndtsson, and S. Lee, 2020. Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors, Journal of Hydrology, 589: 125197.
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Rahmati, O. and A. M. Melesse, 2016. Application of Dempster-Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran, Science of The Total Environment, 568: 1110-1123.
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Rahmati, O., D. D. Moghaddam, V. Moosavi, Z. Kalantari, M. Samadi, S. Lee, and D. Tien Bui, 2019. An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping, Remote Sensing, 11(11): 1375.
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