GIS-BASED GROUNDWATER POTENTIAL MAPPING USING MACHINE LEARNING MODELS, A CASE STUDY: QOM PROVINCE, IRAN

7 citations as recorded by crossref.

1. Groundwater potential mapping in arid and semi-arid regions of kurdistan region of Iraq: A geoinformatics-based machine learning approach K. Fatah et al. https://doi.org/10.1016/j.gsd.2024.101337
2. Evaluation of Groundwater Potential Zones Using GIS‐Based Machine Learning Ensemble Models in the Gidabo Watershed, Ethiopia M. Mussa et al. https://doi.org/10.1002/gch2.202400137
3. Data-Driven Insights into Factors Controlling Groundwater Potential Using Explainable and Ensemble Machine Learning Models M. Kasahun et al. https://doi.org/10.1007/s41748-025-00866-x
4. Bridging Expert and Data-Driven Models: AHP and Scaled Machine Learning for Groundwater Potential Mapping in the FCT, Nigeria J. Ejepu et al. https://doi.org/10.66922/jmg.v62n2.015
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7. Drinking water resources suitability assessment in Brahmani river Odisha based on pollution index of surface water utilizing advanced water quality methods A. Das https://doi.org/10.1038/s41598-025-19539-w