{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:22:33Z","timestamp":1781533353221,"version":"3.54.5"},"reference-count":144,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007899","name":"University of Arizona","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007899","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/03m2x1q45","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>Groundwater is a vital freshwater resource that supports domestic, agricultural, and industrial activities in many regions worldwide. Accurate groundwater potential mapping (GPM) is essential for sustainable water resource management; however, traditional empirical and statistical approaches often struggle to capture the complex, nonlinear relationships among hydrogeological variables. In recent years, machine learning (ML) has emerged as a powerful data-driven approach for improving GPM accuracy and efficiency. This review synthesizes findings from 83 peer-reviewed studies published between 2015 and 2025, focusing on widely used ML algorithms such as Random Forest, Support Vector Machines, Artificial Neural Networks, and hybrid models. The review evaluates key methodological aspects, including input parameter selection, data partitioning, integration with GIS and remote sensing, and model justification techniques. It also discusses common challenges such as data limitations, regional variability, and model interpretability. The results indicate that ML-based approaches can significantly enhance groundwater prediction when supported by appropriate data and validation strategies. Future research directions include explainable artificial intelligence, uncertainty quantification, multi-source data integration, and improved model transferability. This review provides a comprehensive reference for advancing reliable and sustainable groundwater potential mapping.<\/jats:p>","DOI":"10.3390\/w18080947","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T15:56:56Z","timestamp":1776268616000},"page":"947","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Groundwater Potential Mapping Using Machine Learning Techniques: Current Trends and Future Perspectives"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1796-0134","authenticated-orcid":false,"given":"Mosaad Ali Hussein","family":"Ali","sequence":"first","affiliation":[{"name":"Biosystems Engineering Department, University of Arizona, Tucson, AZ 85721, USA"},{"name":"Mining and Metallurgical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71511, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8402-467X","authenticated-orcid":false,"given":"Elsayed Ahmed","family":"Elsadek","sequence":"additional","affiliation":[{"name":"Biosystems Engineering Department, University of Arizona, Tucson, AZ 85721, USA"},{"name":"Agricultural and Biosystems Engineering Department, College of Agriculture, Damietta University, Damietta 34517, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2554-5061","authenticated-orcid":false,"given":"Clinton","family":"Williams","sequence":"additional","affiliation":[{"name":"Arid Land Agricultural Research Center, United States Department of Agriculture (USDA)\u2014Agricultural Research Service (ARS), Maricopa, AZ 85138, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9168-875X","authenticated-orcid":false,"given":"Kelly R.","family":"Thorp","sequence":"additional","affiliation":[{"name":"Grassland Soil & Water Research Laboratory, United States Department of Agriculture (USDA)\u2014Agricultural Research Service (ARS), Temple, TX 76502, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8055-6214","authenticated-orcid":false,"given":"Diaa Eldin M.","family":"Elshikha","sequence":"additional","affiliation":[{"name":"Biosystems Engineering Department, University of Arizona, Tucson, AZ 85721, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37548","DOI":"10.1038\/s41598-025-22130-y","article-title":"Bayesian-Optimized Machine Learning Boosts Actual Evapotranspiration Prediction in Water-Stressed Agricultural Regions of China","volume":"15","author":"Elbeltagi","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Elsadek, E.A., Attalah, S., Williams, C., Thorp, K.R., Wang, D., and Elshikha, D.E.M. (2026). Comparing Cotton ET Data from a Satellite Platform, In Situ Sensor, and Soil Water Balance Method in Arizona. Agriculture, 16.","DOI":"10.3390\/agriculture16020228"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Attalah, S., Elsadek, E.A., Waller, P., Hunsaker, D., Thorp, K., Bautista, E., Williams, C., Wall, G., Orr, E., and Elshikha, D.E. (2024). Evaluating the Performance of OpenET Models for Alfalfa in Arizona. Proceedings of the 2024 Anaheim, Anaheim, CA, USA, 28\u201331 July 2024, American Society of Agricultural and Biological Engineers.","DOI":"10.13031\/aim.202400041"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108673","DOI":"10.1016\/j.agwat.2024.108673","article-title":"Impacts of Climate Change on Rice Yields in the Nile River Delta of Egypt: A Large-Scale Projection Analysis Based on CMIP6","volume":"292","author":"Elsadek","year":"2024","journal-title":"Agric. Water Manag."},{"key":"ref_5","unstructured":"Elsadek, E.A. (2023). Study on the In-Field Water Balance and the Projected Impacts of Climate Change on Rice Yields in the Nile River Delta. [Ph.D. Thesis, Hohai University]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Elshikha, D.E., Attalah, S., Elsadek, E.A., Waller, P., Thorp, K., Sanyal, D., Bautista, E., Norton, R., Hunsaker, D., and Williams, C. (2024). The Impact of Gravity Drip and Flood Irrigation on Development, Water Productivity, and Fiber Yield of Cotton in Semi-Arid Conditions of Arizona. Proceedings of the 2024 Anaheim, Anaheim, CA, USA, 28\u201331 July 2024, American Society of Agricultural and Biological Engineers.","DOI":"10.13031\/aim.202400004"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s00271-025-01019-8","article-title":"Projecting Rice Water Footprint for Different Shared Socioeconomic Pathways Under Arid Climate Conditions","volume":"43","author":"Elsadek","year":"2025","journal-title":"Irrig. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gouertoumbo, F.W., Alhaj Hamoud, Y., Guo, X., Shaghaleh, H., Ali Adam Hamad, A., and Elsadek, E. (2022). Wheat Straw Burial Enhances the Root Physiology, Productivity, and Water Utilization Efficiency of Rice Under Alternative Wetting and Drying Irrigation. Sustainability, 14.","DOI":"10.3390\/su142416394"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mostafa, A.E., Ali, M.A.M., Ali, F.A., Rabeiy, R., Saleem, H.A., Shebl, A., and Ali, M.A.H. (2025). Groundwater Potential Mapping in Semi-Arid Areas Using Integrated Remote Sensing, GIS, and Geostatistics Techniques. Water, 17.","DOI":"10.3390\/w17131909"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101891","DOI":"10.1016\/j.ejrh.2024.101891","article-title":"Exploring Groundwater Patterns in Souss-Massa Mountainous Basin, Morocco: A Fusion of Fractal Analysis and Machine Learning Techniques on Gravity Data","volume":"54","author":"Echogdali","year":"2024","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hosseini, F.S., Jafari, A., Zandi, I., Alesheikh, A.A., and Rezaie, F. (2025). Groundwater Potential Mapping Using Optimized Decision Tree-Based Ensemble Learning Model with Local and Global Explainability. Water, 17.","DOI":"10.3390\/w17101520"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"14781","DOI":"10.1007\/s10668-024-05832-7","article-title":"Evaluating the Effects of Rapid Urbanization on the Encroachment of the East Kolkata Wetland Ecosystem: A Remote Sensing and Hybrid Machine Learning Approach","volume":"27","author":"Mondal","year":"2025","journal-title":"Environ. Dev. Sustain."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1038\/s43017-022-00378-6","article-title":"Global Water Resources and the Role of Groundwater in a Resilient Water Future","volume":"4","author":"Scanlon","year":"2023","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Elmahdy, S., Ali, T., and Mohamed, M. (2021). Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model. Remote Sens., 13.","DOI":"10.3390\/rs13122300"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"23671","DOI":"10.1038\/s41598-025-08030-1","article-title":"Environmental Impact Assessment of Leachate from Mining Tailings Using Electrical Resistivity Imaging","volume":"15","author":"Ali","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.2166\/nh.2023.083","article-title":"Application of Advanced Machine Learning Algorithms and Geospatial Techniques for Groundwater Potential Zone Mapping in Gambela Plain, Ethiopia","volume":"54","author":"Seifu","year":"2023","journal-title":"Hydrol. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ouali, L., Kabiri, L., Namous, M., Hssaisoune, M., Abdelrahman, K., Fnais, M.S., Kabiri, H., El Hafyani, M., Oubaassine, H., and Arioua, A. (2023). Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco. Sustainability, 15.","DOI":"10.3390\/su15053874"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s13201-022-01571-0","article-title":"Novel Hybrid Models to Enhance the Efficiency of Groundwater Potentiality Model","volume":"12","author":"Talukdar","year":"2022","journal-title":"Appl. Water Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1007\/s10040-019-02001-3","article-title":"Review: Advances in Groundwater Potential Mapping","volume":"27","year":"2019","journal-title":"Hydrogeol. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"10328","DOI":"10.1038\/s41598-024-60560-2","article-title":"Future Groundwater Potential Mapping Using Machine Learning Algorithms and Climate Change Scenarios in Bangladesh","volume":"14","author":"Sarkar","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14495","DOI":"10.1080\/10106049.2022.2088861","article-title":"Developing a New Method for Future Groundwater Potentiality Mapping Under Climate Change in Bisha Watershed, Saudi Arabia","volume":"37","author":"Mallick","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101980","DOI":"10.1016\/j.ecoinf.2023.101980","article-title":"Machine Learning and GIS-RS-Based Algorithms for Mapping the Groundwater Potentiality in the Bundelkhand Region, India","volume":"74","author":"Kumar","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1080\/02626667.2021.1906427","article-title":"Integrated Multi-Criteria Analysis for Groundwater Potential Mapping in Precambrian Hard Rock Terranes (North Gujarat), India","volume":"66","author":"Pradhan","year":"2021","journal-title":"Hydrol. Sci. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.catena.2018.10.037","article-title":"Potential Groundwater Zone Mapping Based on Geo-Hydrological Considerations and Multi-Criteria Spatial Analysis: North UAE","volume":"173","author":"Shanableh","year":"2019","journal-title":"Catena"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Elsadek, E.A., Ali, M.A.H., Williams, C., Thorp, K.R., and Elshikha, D.E.M. (2025). A Novel Framework for Predicting Daily Reference Evapotranspiration Using Interpretable Machine Learning Techniques. Agriculture, 15.","DOI":"10.3390\/agriculture15181985"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/s13201-024-02308-x","article-title":"Advanced Reference Crop Evapotranspiration Prediction: A Novel Framework Combining Neural Nets, Bee Optimization Algorithm, and Mode Decomposition","volume":"14","author":"Elbeltagi","year":"2024","journal-title":"Appl. Water Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1007\/s13201-025-02407-3","article-title":"An Ensemble Model of Knowledge- and Data-Driven Geospatial Methods for Mapping Groundwater Potential in a Data-Scarce, Semi-Arid Fractured Rock Region","volume":"15","author":"Fildes","year":"2025","journal-title":"Appl. Water Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s12665-024-11996-2","article-title":"Assessing Groundwater Potentialities and Replenishment Feasibility Using Machine Learning and MCDM Models Considering Hydro-Geological Aspects and Water Quality Constituents","volume":"84","author":"Kanji","year":"2025","journal-title":"Environ. Earth Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"26186","DOI":"10.1038\/s41598-024-76607-3","article-title":"An Integrated Approach of Support Vector Machine (SVM) and Weight of Evidence (WOE) Techniques to Map Groundwater Potential and Assess Water Quality","volume":"14","author":"Riaz","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pappaka, R.K., Nakkala, A.B., Badapalli, P.K., Gugulothu, S., Anguluri, R., Hasher, F.F.B., and Zhran, M. (2025). Machine Learning-Driven Groundwater Potential Zoning Using Geospatial Analytics and Random Forest in the Pandameru River Basin, South India. Sustainability, 17.","DOI":"10.3390\/su17093851"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Maskooni, E.K., Naghibi, S.A., Hashemi, H., and Berndtsson, R. (2020). Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data. Remote Sens., 12.","DOI":"10.3390\/rs12172742"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"15063","DOI":"10.1080\/10106049.2022.2093992","article-title":"Delineation of Groundwater Potential Zones of Upper Godavari Sub-Basin of India Using Bi-Variate, MCDM and Advanced Machine Learning Algorithms","volume":"37","author":"Choudhary","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_33","first-page":"272","article-title":"Groundwater Potential Zoning Using Logistics Model Trees Based Novel Ensemble Machine Learning Model","volume":"46","author":"Bien","year":"2024","journal-title":"Vitnam J. Earth Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"404","DOI":"10.3151\/jact.20.404","article-title":"Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review","volume":"20","author":"Jin","year":"2022","journal-title":"J. Adv. Concr. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102975","DOI":"10.1016\/j.asej.2024.102975","article-title":"Prediction of Compressive Strength of Recycled Concrete Using Gradient Boosting Models","volume":"15","author":"Ahmed","year":"2024","journal-title":"Ain Shams Eng. J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Masroor, M., Sajjad, H., Kumar, P., Saha, T.K., Rahaman, M.H., Choudhari, P., Kulimushi, L.C., Pal, S., and Saito, O. (2023). Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India. Water, 15.","DOI":"10.3390\/w15030419"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2549","DOI":"10.1007\/s11053-022-10100-4","article-title":"Groundwater Potential Mapping in Hubei Region of China Using Machine Learning, Ensemble Learning, Deep Learning and AutoML Methods","volume":"31","author":"Bai","year":"2022","journal-title":"Nat. Resour. Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ali, M.A.H., Mewafy, F.M., Qian, W., Faruwa, A.R., Shebl, A., Dabaa, S., and Saleem, H.A. (2024). Numerical Simulation of Geophysical Models to Detect Mining Tailings\u2019 Leachates within Tailing Storage Facilities. Water, 16.","DOI":"10.3390\/w16050753"},{"key":"ref_39","first-page":"489","article-title":"Gis-Based Groundwater Potential Mapping Using Machine Learning Models, a Case Study: Qom Province, Iran","volume":"10","author":"Masoudian","year":"2023","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1007\/s11269-015-1114-8","article-title":"A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping","volume":"29","author":"Naghibi","year":"2015","journal-title":"Water Resour. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, P.X., Masoumi, Z., Kalantari, M., Aflaki, M., and Mansourian, A. (2022). A GIS-Based Landslide Susceptibility Mapping and Variable Importance Analysis Using Artificial Intelligent Training-Based Methods. Remote Sens., 14.","DOI":"10.3390\/rs14010211"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1007\/s10661-015-5049-6","article-title":"GIS-Based Groundwater Potential Mapping Using Boosted Regression Tree, Classification and Regression Tree, and Random Forest Machine Learning Models in Iran","volume":"188","author":"Naghibi","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1007\/s40808-016-0150-6","article-title":"Spatial Mapping of Artesian Zone at Iraqi Southern Desert Using a GIS-Based Random Forest Machine Learning Model","volume":"2","author":"Shahid","year":"2016","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1007\/s13201-022-01584-9","article-title":"Groundwater Potential Mapping Using Multi-Criteria Decision, Bivariate Statistic and Machine Learning Algorithms: Evidence from Chota Nagpur Plateau, India","volume":"12","author":"Hasanuzzaman","year":"2022","journal-title":"Appl. Water Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Park, S., Hamm, S.Y., Jeon, H.T., and Kim, J. (2017). Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS. Sustainability, 9.","DOI":"10.3390\/su9071157"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"221","DOI":"10.5194\/hess-26-221-2022","article-title":"Preprocessing Approaches in Machine-Learning-Based Groundwater Potential Mapping: An Application to the Koulikoro and Bamako Regions, Mali","volume":"26","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.scitotenv.2018.12.115","article-title":"GIS-Based Groundwater Potential Mapping in Shahroud Plain, Iran. A Comparison among Statistical (Bivariate and Multivariate), Data Mining and MCDM Approaches","volume":"658","author":"Arabameri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7927","DOI":"10.1080\/10106049.2021.1987535","article-title":"Developing Groundwater Potentiality Models by Coupling Ensemble Machine Learning Algorithms and Statistical Techniques for Sustainable Groundwater Management","volume":"37","author":"Mallick","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Roy, J., Saha, S., Blaschke, T., Ghorbanzadeh, O., and Bui, D.T. (2019). Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran. Remote Sens., 11.","DOI":"10.3390\/rs11243015"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s11053-018-9416-1","article-title":"Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas","volume":"28","author":"Sameen","year":"2019","journal-title":"Nat. Resour. Res."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Madani, A., and Niyazi, B. (2023). Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia. Sustainability, 15.","DOI":"10.3390\/su15032772"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kalantar, B., Al-Najjar, H.A.H., Pradhan, B., Saeidi, V., Halin, A.A., Ueda, N., and Naghibi, S.A. (2019). Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping. Water, 11.","DOI":"10.3390\/w11091909"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"63991","DOI":"10.1007\/s11356-023-26961-y","article-title":"The Potential Evaluation of Groundwater by Integrating Rank Sum Ratio (RSR) and Machine Learning Algorithms in the Qaidam Basin","volume":"30","author":"Wang","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1007\/s11053-019-09465-w","article-title":"Novel Hybrid Integration Approach of Bagging-Based Fisher\u2019s Linear Discriminant Function for Groundwater Potential Analysis","volume":"28","author":"Chen","year":"2019","journal-title":"Nat. Resour. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2426","DOI":"10.2166\/ws.2023.134","article-title":"Identification of Groundwater Potential Zones of Idukki District Using Remote Sensing and GIS-Based Machine-Learning Approach","volume":"23","author":"Khan","year":"2023","journal-title":"Water Supply"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Rahmati, O., Moghaddam, D.D., Moosavi, V., Kalantari, Z., Samadi, M., Lee, S., and Bui, D.T. (2019). An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping. Remote Sens., 11.","DOI":"10.3390\/rs11111375"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s12145-022-00925-1","article-title":"Groundwater Potential Mapping in the Central Highlands of Vietnam Using Spatially Explicit Machine Learning","volume":"16","author":"Bien","year":"2023","journal-title":"Earth Sci. Inform."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1007\/s12665-020-08944-1","article-title":"A Comparison of Machine Learning Models for the Mapping of Groundwater Spring Potential","volume":"79","author":"Pourghasemi","year":"2020","journal-title":"Environ. Earth Sci."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Shandu, I.D., and Atif, I. (2023). An Integration of Geospatial Modelling and Machine Learning Techniques for Mapping Groundwater Potential Zones in Nelson Mandela Bay, South Africa. Water, 15.","DOI":"10.3390\/w15193447"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Nguyen, P.T., Ha, D.H., Nguyen, H.D., Phong, T.V., Trinh, P.T., Al-Ansari, N., Le, H.V., Pham, B.T., Ho, L.S., and Prakash, I. (2020). Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling. Sustainability, 12.","DOI":"10.3390\/su12072622"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Namous, M., Hssaisoune, M., Pradhan, B., Lee, C.W., Alamri, A., Elaloui, A., Edahbi, M., Krimissa, S., Eloudi, H., and Ouayah, M. (2021). Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models. Water, 13.","DOI":"10.3390\/w13162273"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40562-023-00261-2","article-title":"Using an Ensemble Machine Learning Model to Delineate Groundwater Potential Zones in Desert Fringes of East Esna-Idfu Area, Nile Valley, Upper Egypt","volume":"10","author":"Morgan","year":"2023","journal-title":"Geosci. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Moghaddam, D.D., Rahmati, O., Haghizadeh, A., and Kalantari, Z. (2020). A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models. Water, 12.","DOI":"10.3390\/w12030679"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Trabelsi, F., Bel Hadj Ali, S., and Lee, S. (2023). Comparison of Novel Hybrid and Benchmark Machine Learning Algorithms to Predict Groundwater Potentiality: Case of a Drought-Prone Region of Medjerda Basin, Northern Tunisia. Remote Sens., 15.","DOI":"10.3390\/rs15010152"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Nguyen, P.T., Ha, D.H., Jaafari, A., Nguyen, H.D., and Phong, T. (2020). Van Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-Study, Vietnam. Int. J. Environ. Res. Public. Health, 17.","DOI":"10.3390\/ijerph17072473"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Nguyen, P.T., Ha, D.H., Avand, M., Jaafari, A., Nguyen, H.D., Al-Ansari, N., Phong, T.V., Sharma, R., Kumar, R., and Le, H.V. (2020). Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Appl. Sci., 10.","DOI":"10.3390\/app10072469"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.1007\/s11600-023-01237-8","article-title":"Effectiveness of Machine Learning Ensemble Models in Assessing Groundwater Potential in Lidder Watershed, India","volume":"72","author":"Ali","year":"2024","journal-title":"Acta Geophys."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1007\/s12665-021-09725-0","article-title":"Assessment of Groundwater Potential in Terms of the Availability and Quality of the Resource: A Case Study from Iraq","volume":"80","author":"Fryar","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Bi, B.T., Li, J.W., Luo, T.Y., Wang, B., Yang, C., and Shen, L.N. (2025). Positive-Unlabeled Learning-Based Hybrid Models and Interpretability for Groundwater Potential Mapping in Karst Areas. Water, 17.","DOI":"10.3390\/w17101422"},{"key":"ref_70","first-page":"1403","article-title":"Spatial Predictions of Groundwater Potential Using Automated Machine Learning (AutoML): A Comparative Study of Feature Selection and Training Sample Size in Qinghai Province, China","volume":"31","author":"Wang","year":"2024","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s11600-023-01042-3","article-title":"Assessing the Nature of Potential Groundwater Zones through Machine Learning (ML) Algorithm in Tropical Plateau Region, West Bengal, India","volume":"72","author":"Kundu","year":"2024","journal-title":"Acta Geophys."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1007\/s11269-021-02815-5","article-title":"K-Fold and State-of-the-Art Metaheuristic Machine Learning Approaches for Groundwater Potential Modelling","volume":"35","author":"Arabameri","year":"2021","journal-title":"Water Resour. Manag."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Jari, A., Bachaoui, E., Hajaj, S., Khaddari, A., Khandouch, Y., El Harti, A., Jellouli, A., and Namous, M. (2023). Investigating Machine Learning and Ensemble Learning Models in Groundwater Potential Mapping in Arid Region: Case Study from Tan-Tan Water-Scarce Region, Morocco. Front. Water, 5.","DOI":"10.3389\/frwa.2023.1305998"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1007\/s10661-021-09519-8","article-title":"A Novel Ensemble Model of Automatic Multilayer Perceptron, Random Forest, and ZeroR for Groundwater Potential Mapping","volume":"193","author":"Sachdeva","year":"2021","journal-title":"Environ. Monit. Assess."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"AlAyyash, S., Al-Fugara, A., Shatnawi, R., Al-Shabeeb, A.R., Al-Adamat, R., and Al-Amoush, H. (2023). Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping. Sustainability, 15.","DOI":"10.3390\/su15032499"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"4415","DOI":"10.1007\/s11269-021-02957-6","article-title":"Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping","volume":"35","author":"Ha","year":"2021","journal-title":"Water Resour. Manag."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"e2023EA003338","DOI":"10.1029\/2023EA003338","article-title":"Novel Ensemble Models Based on the Split-Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping","volume":"11","author":"Wang","year":"2024","journal-title":"Earth Sp. Sci."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Karimi-Rizvandi, S., Goodarzi, H.V., Afkoueieh, J.H., Chung, I.M., Kisi, O., Kim, S., and Linh, N.T.T. (2021). Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms. Water, 13.","DOI":"10.3390\/w13050658"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s12665-021-10116-8","article-title":"Characterization of Groundwater Potential Zones in Water-Scarce Hardrock Regions Using Data Driven Model","volume":"80","author":"Ruidas","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2400137","DOI":"10.1002\/gch2.202400137","article-title":"Evaluation of Groundwater Potential Zones Using GIS-Based Machine Learning Ensemble Models in the Gidabo Watershed, Ethiopia","volume":"8","author":"Mussa","year":"2024","journal-title":"Glob. Chall."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Al-Ozeer, A.Z., Al-Abadi, A.M., Hussain, T.A., Fryar, A.E., Pradhan, B., Alamri, A., and Maulud, K.N.A. (2021). Modeling of Groundwater Potential Using Cloud Computing Platform: A Case Study from Nineveh Plain, Northern Iraq. Water, 13.","DOI":"10.3390\/w13233330"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"4395","DOI":"10.1007\/s11600-024-01331-5","article-title":"Integrated Machine Learning and Remote Sensing for Groundwater Potential Mapping in the Mekong Delta in Vietnam","volume":"72","author":"Nguyen","year":"2024","journal-title":"Acta Geophys."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Djerida, A. (2022, January 17\u201322). Investigating Groundwater Zones Relationship with Rainfall, Temperature and Modis-Derived NDVI and EVI. Proceedings of the IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884663"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1186\/s12302-024-00981-y","article-title":"Application of Bagging and Boosting Ensemble Machine Learning Techniques for Groundwater Potential Mapping in a Drought-Prone Agriculture Region of Eastern India","volume":"36","author":"Halder","year":"2024","journal-title":"Environ. Sci. Eur."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"104421","DOI":"10.1016\/j.catena.2019.104421","article-title":"The Effect of Sample Size on Different Machine Learning Models for Groundwater Potential Mapping in Mountain Bedrock Aquifers","volume":"187","author":"Moghaddam","year":"2020","journal-title":"Catena"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1007\/s12145-023-01209-y","article-title":"Application of Hybrid Model-Based Machine Learning for Groundwater Potential Prediction in the North Central of Vietnam","volume":"17","author":"Nguyen","year":"2024","journal-title":"Earth Sci. Inform."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1007\/s12524-022-01582-z","article-title":"Application of Machine Learning and Geospatial Techniques for Groundwater Potential Mapping","volume":"50","author":"Saha","year":"2022","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s10040-022-02567-5","article-title":"Enhancing the Prediction of Hydraulic Parameters Using Machine Learning, Integrating Multiple Attributes of GIS and Geophysics","volume":"31","author":"Gupta","year":"2023","journal-title":"Hydrogeol. J."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Hamdi, M., El Alem, A., and Goita, K. (2025). Groundwater Storage Estimation in the Saskatchewan River Basin Using GRACE\/GRACE-FO Gravimetric Data and Machine Learning. Atmosphere, 16.","DOI":"10.3390\/atmos16010050"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"8924","DOI":"10.1080\/10106049.2021.2007298","article-title":"Delineation of Groundwater Potential Zones by Means of Ensemble Tree Supervised Classification Methods in the Eastern Lake Chad Basin","volume":"37","author":"Elisa","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_91","first-page":"101245","article-title":"Multiclass Spatial Predictions of Borehole Yield in Southern Mali by Means of Machine Learning Classifiers","volume":"44","author":"Diancoumba","year":"2022","journal-title":"J. Hydrol. Stud."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.1007\/s00477-025-02941-1","article-title":"Optimizing Groundwater Potential Assessment: Uncertainty Reduction through Sample Balancing and Enhanced Hybrid Modeling","volume":"39","author":"Liu","year":"2025","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"5564","DOI":"10.1080\/10106049.2021.1920635","article-title":"Evaluation Efficiency of Hybrid Deep Learning Algorithms with Neural Network Decision Tree and Boosting Methods for Predicting Groundwater Potential","volume":"37","author":"Chen","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Al-Kindi, K.M., and Janizadeh, S. (2022). Machine Learning and Hyperparameters Algorithms for Identifying Groundwater Aflaj Potential Mapping in Semi-Arid Ecosystems Using LiDAR, Sentinel-2, GIS Data, and Analysis. Remote Sens., 14.","DOI":"10.3390\/rs14215425"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s41101-025-00401-z","article-title":"Integrating Hydrospatial Variables with Machine Learning for Groundwater Potential Zonation in Dhalai District, Tripura","volume":"10","author":"Jaman","year":"2025","journal-title":"Water Conserv. Sci. Eng."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Sekar, S., Surendran, S., Roy, P.D., Dar, F.A., Nath, A.V., Jothimani, M., and Perumal, M. (2025). Appraisal of Groundwater Potential Zones at Melur in Madurai District (Tamil Nadu State) in India for Sustainable Water Resource Management. Water, 17.","DOI":"10.3390\/w17081235"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"6882","DOI":"10.2166\/ws.2022.283","article-title":"Application of Machine Learning to Groundwater Spring Potential Mapping Using Averaging, Bagging, and Boosting Techniques","volume":"22","author":"Wei","year":"2022","journal-title":"Water Supply"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"3635","DOI":"10.1007\/s12524-025-02177-0","article-title":"Prediction of Groundwater Potential Zone Using Machine Learning and Geospatial Approaches for an Industry-Dominated Area in Narayanganj, Bangladesh","volume":"53","author":"Hasan","year":"2025","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s12665-022-10593-5","article-title":"Integration of Group Method of Data Handling (GMDH) Algorithm and Population-Based Metaheuristic Algorithms for Spatial Prediction of Potential Groundwater","volume":"81","author":"Karimipour","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"12042","DOI":"10.1080\/10106049.2022.2063408","article-title":"Identification of Groundwater Potential Zones Using Remote Sensing, GIS, Machine Learning and Electrical Resistivity Tomography Techniques in Guelma Basin, Northeastern Algeria","volume":"37","author":"Braham","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s13201-022-01599-2","article-title":"Combining High Resolution Input and Stacking Ensemble Machine Learning Algorithms for Developing Robust Groundwater Potentiality Models in Bisha Watershed, Saudi Arabia","volume":"12","author":"Mallick","year":"2022","journal-title":"Appl. Water Sci."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"101281","DOI":"10.1016\/j.gsd.2024.101281","article-title":"Mapping and Modeling Groundwater Potential Using Machine Learning, Deep Learning and Ensemble Learning Models in the Saiss Basin (Fez-Meknes Region, Morocco)","volume":"26","author":"Ragragui","year":"2024","journal-title":"Groundw. Sustain. Dev."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1007\/s11063-020-10351-3","article-title":"Specialization in Hierarchical Learning Systems: A Unified Information-Theoretic Approach for Supervised, Unsupervised and Reinforcement Learning","volume":"52","author":"Hihn","year":"2020","journal-title":"Neural Process. Lett."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"5273","DOI":"10.1007\/s40808-024-02063-7","article-title":"Comparative Analysis of Intelligent Models for Predicting Compressive Strength in Recycled Aggregate Concrete","volume":"10","author":"Ahmed","year":"2024","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Ali, M.A.H., Mewafy, F.M., Qian, W., Alshehri, F., Almadani, S., Aldawsri, M., Aloufi, M., and Saleem, H.A. (2023). Mapping Leachate Pathways in Aging Mining Tailings Pond Using Electrical Resistivity Tomography. Minerals, 13.","DOI":"10.3390\/min13111437"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.jhydrol.2017.03.020","article-title":"A Comparative Assessment of GIS-Based Data Mining Models and a Novel Ensemble Model in Groundwater Well Potential Mapping","volume":"548","author":"Naghibi","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"107405","DOI":"10.1016\/j.engappai.2023.107405","article-title":"AutoML-GWL: Automated Machine Learning Model for the Prediction of Groundwater Level","volume":"127","author":"Singh","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"124989","DOI":"10.1016\/j.jhydrol.2020.124989","article-title":"Estimation of Total Dissolved Solids (TDS) Using New Hybrid Machine Learning Models","volume":"587","author":"Banadkooki","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"100990","DOI":"10.1016\/j.ejrh.2022.100990","article-title":"Convolutional Neural Network and Long Short-Term Memory Algorithms for Groundwater Potential Mapping in Anseong, South Korea","volume":"39","author":"Hakim","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Lee, S., Hyun, Y., Lee, S., and Lee, M.J. (2020). Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques. Remote Sens., 12.","DOI":"10.3390\/rs12071200"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s12040-024-02462-5","article-title":"Enhancing Groundwater Potential Evaluation: Integrating Borehole Log Data with Hybrid-MCDM Approach","volume":"133","author":"Mallik","year":"2024","journal-title":"J. Earth Syst. Sci."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s40808-022-01502-7","article-title":"An Integrated Geoinformatics and Hydrogeological Approach to Delineating Groundwater Potential Zones in the Complex Geological Terrain of Abuja, Nigeria","volume":"9","author":"Etuk","year":"2023","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"107685","DOI":"10.1016\/j.ecolind.2021.107685","article-title":"Geospatial Mapping of Groundwater Potential Zones Using Multi-Criteria Decision-Making AHP Approach in a Hardrock Basaltic Terrain in India","volume":"127","author":"Doke","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s13201-023-02056-4","article-title":"Modeling of Geophysical Derived Parameters for Groundwater Potential Zonation Using GIS-Based Multi-Criteria Conceptual Model","volume":"14","author":"Bayode","year":"2024","journal-title":"Appl. Water Sci."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1002\/hyp.9803","article-title":"Comparison of Three Methods of Hydrogeological Parameter Estimation in Leaky Aquifers Using Transient Flow Pumping Tests","volume":"28","author":"Li","year":"2014","journal-title":"Hydrol. Process."},{"key":"ref_116","first-page":"e02549","article-title":"Geospatial Mapping and Multi-Criteria Analysis of Groundwater Potential in Libo Kemkem Watershed, Upper Blue Nile River Basin, Ethiopia","volume":"27","author":"Tebege","year":"2025","journal-title":"Sci. Afr."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Yihdego, Y., Webb, J.A., and Vaheddoost, B. (2017). Highlighting the Role of Groundwater in Lake- Aquifer Interaction to Reduce Vulnerability and Enhance Resilience to Climate Change. Hydrology, 4.","DOI":"10.3390\/hydrology4010010"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1111\/gwat.12939","article-title":"Mapping Groundwater Potential Through an Ensemble of Big Data Methods","volume":"58","author":"Renard","year":"2020","journal-title":"Groundwater"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1080\/15481603.2020.1794104","article-title":"Application of Machine Learning Techniques in Groundwater Potential Mapping along the West Coast of India","volume":"57","author":"Prasad","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Fadhillah, M.F., Lee, S., Lee, C.W., and Park, Y.C. (2021). Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-Si, South Korea. Remote Sens., 13.","DOI":"10.3390\/rs13061196"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"2216852","DOI":"10.1080\/19475705.2023.2216852","article-title":"Comparative Analysis of GIS and RS Based Models for Delineation of Groundwater Potential Zone Mapping","volume":"14","author":"Islam","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"127977","DOI":"10.1016\/j.jhydrol.2022.127977","article-title":"Spatial Prediction of Groundwater Potentiality Using Machine Learning Methods with Grey Wolf and Sparrow Search Algorithms","volume":"610","author":"Liu","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"5587","DOI":"10.1038\/s41598-021-85205-6","article-title":"Groundwater Recharge Potential Zonation Using an Ensemble of Machine Learning and Bivariate Statistical Models","volume":"11","author":"Jaafarzadeh","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"102945","DOI":"10.1016\/j.ecoinf.2024.102945","article-title":"Exploration of Slope-Type Geological Hazard Susceptibility Evaluation Based on Dynamic Correction of SBAS-InSAR Technology: A Case Study of Kang County in Gansu Province","volume":"85","author":"Li","year":"2025","journal-title":"Ecol. Inform."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1080\/15481603.2016.1169741","article-title":"Remote Sensing and Information Value (IV) Model for Regional Mapping of Fluvial Channels and Topographic Wetness in the Saudi Arabia","volume":"53","author":"Mohamed","year":"2016","journal-title":"GIScience Remote Sens."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Ding, Q., Wang, F., Chen, J., Wang, M., and Zhang, X. (2022). Research on Generalized RQD of Rock Mass Based on 3D Slope Model Established by Digital Close-Range Photogrammetry. Remote Sens., 14.","DOI":"10.3390\/rs14092275"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1080\/10106049.2020.1861664","article-title":"Assessment of Gini-, Entropy- and Ratio-Based Classification Trees for Groundwater Potential Modelling and Prediction","volume":"37","author":"Rahmati","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1080\/10106049.2016.1170892","article-title":"Investigation of Automatic Feature Weighting Methods (Fisher, Chi-Square and Relief-F) for Landslide Susceptibility Mapping","volume":"32","author":"Ipbuker","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"13736","DOI":"10.1007\/s11356-020-11158-4","article-title":"Probability Mapping of Groundwater Contamination by Hydrocarbon from the Deep Oil Reservoirs Using GIS-Based Machine-Learning Algorithms: A Case Study of the Dammam Aquifer (Middle of Iraq)","volume":"28","author":"Fryar","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1007\/s11269-020-02603-7","article-title":"Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping","volume":"34","author":"Yariyan","year":"2020","journal-title":"Water Resour. Manag."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Akbar, T.A., Javed, A., Ullah, S., Ullah, W., Pervez, A., Akbar, R.A., Javed, M.F., Mohamed, A., and Mohamed, A.M. (2022). Principal Component Analysis (PCA)\u2013Geographic Information System (GIS) Modeling for Groundwater and Associated Health Risks in Abbottabad, Pakistan. Sustainability, 14.","DOI":"10.3390\/su142114572"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"e36606","DOI":"10.1016\/j.heliyon.2024.e36606","article-title":"Comprehensive Evaluation and Prediction of Groundwater Quality and Risk Indices Using Quantitative Approaches, Multivariate Analysis, and Machine Learning Models: An Exploratory Study","volume":"10","author":"Gad","year":"2024","journal-title":"Heliyon"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.jhydrol.2012.02.025","article-title":"Principal Component Analysis of Time Series for Identifying Indicator Variables for Riverine Groundwater Extraction Management","volume":"432\u2013433","author":"Page","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"100930","DOI":"10.1016\/j.ejrh.2021.100930","article-title":"Impacts of Regional Characteristics on Improving the Accuracy of Groundwater Level Prediction Using Machine Learning: The Case of Central Eastern Continental United States","volume":"37","author":"Cai","year":"2021","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"113747","DOI":"10.1016\/j.envres.2022.113747","article-title":"Forecasting Groundwater Level of Karst Aquifer in a Large Mining Area Using Partial Mutual Information and NARX Hybrid Model","volume":"213","author":"Zhang","year":"2022","journal-title":"Environ. Res."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s00254-003-0917-8","article-title":"A Comparison of the GIS Based Landslide Susceptibility Assessment Methods: Multivariate versus Bivariate","volume":"45","author":"Doyuran","year":"2004","journal-title":"Environ. Geol."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Park, S., and Kim, J. (2021). The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential. Sustainability, 13.","DOI":"10.3390\/su13052459"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"110525","DOI":"10.1016\/j.jenvman.2020.110525","article-title":"Using Machine Learning Algorithms to Map the Groundwater Recharge Potential Zones","volume":"265","author":"Pourghasemi","year":"2020","journal-title":"J. Environ. Manage."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Shen, D., Cao, Y., Wang, X., Zhang, B., and Dong, H. (2025). An Ensemble Machine Learning Approach for High-Resolution Estimation of Groundwater Storage Anomalies. Water, 17.","DOI":"10.3390\/w17101445"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1007\/s11600-023-01053-0","article-title":"Groundwater Spring Potential Prediction Using a Deep-Learning Algorithm","volume":"72","author":"Moughani","year":"2024","journal-title":"Acta Geophys."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"101172","DOI":"10.1016\/j.gsd.2024.101172","article-title":"Physics-Informed Neural Networks in Groundwater Flow Modeling: Advantages and Future Directions","volume":"25","author":"Ali","year":"2024","journal-title":"Groundw. Sustain. Dev."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1080\/17538947.2020.1718785","article-title":"A Tree-Based Intelligence Ensemble Approach for Spatial Prediction of Potential Groundwater","volume":"13","author":"Avand","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Farzin, M., Avand, M., Ahmadzadeh, H., Zelenakova, M., and Tiefenbacher, J.P. (2021). Assessment of Ensemble Models for Groundwater Potential Modeling and Prediction in a Karst Watershed. Water, 13.","DOI":"10.3390\/w13182540"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Panjala, P., Gumma, M.K., Ajeigbe, H.A., Badamasi, M.M., Deevi, K.C., and Tabo, R. (2022). Identifying Suitable Watersheds Across Nigeria Using Biophysical Parameters and Machine Learning Algorithms for Agri\u2013Planning. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11080416"}],"container-title":["Water"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4441\/18\/8\/947\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:12:06Z","timestamp":1776399126000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4441\/18\/8\/947"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,15]]},"references-count":144,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["w18080947"],"URL":"https:\/\/doi.org\/10.3390\/w18080947","relation":{},"ISSN":["2073-4441"],"issn-type":[{"value":"2073-4441","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,15]]}}}