34 citations as recorded by crossref.

1. Spatial prediction of groundwater potential by various novel boosting-based ensemble learning models in mountainous areas H. Xiong et al. 10.1080/10106049.2023.2274870
2. Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables H. Touré et al. 10.1007/s43832-024-00109-6
3. Groundwater potential mapping in arid and semi-arid regions of kurdistan region of Iraq: A geoinformatics-based machine learning approach K. Fatah et al. 10.1016/j.gsd.2024.101337
4. Assessment of potential health risks from heavy metal pollution of surface water for drinking in a multi-industry area in Mali using a multi-indices approach L. Sangaré et al. 10.1007/s10661-023-11258-x
5. Integrated machine learning and remote sensing for groundwater potential mapping in the Mekong Delta in Vietnam H. Nguyen et al. 10.1007/s11600-024-01331-5
6. Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco L. Ouali et al. 10.3390/su15053874
7. Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learning K. Dahal et al. 10.1016/j.jhydrol.2023.130417
8. Groundwater potential zoning by integrating multi-criteria decision and bivariate analysis methods – a case study on Cheyyar River Basin, Tamil Nadu, India V. Narayanamurthi & A. Ramasamy 10.1080/10106049.2022.2088864
9. Potential of machine learning algorithms in groundwater level prediction using temporal gravity data H. Sarkar et al. 10.1016/j.gsd.2024.101114
10. Harnessing machine learning for assessing climate change influences on groundwater resources: A comprehensive review A. Bamal et al. 10.1016/j.heliyon.2024.e37073
11. Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers . Gómez-Escalonilla et al. 10.1016/j.ejrh.2022.101245
12. Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms X. Guo et al. 10.1016/j.jhydrol.2023.129599
13. Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India K. Halder et al. 10.1186/s12302-024-00981-y
14. Delineation of Groundwater Potential Using the Bivariate Statistical Models and Hybridized Multi-Criteria Decision-Making Models M. Baduna Koçyiğit & H. Akay 10.3390/w16223273
15. Developing a new method for future groundwater potentiality mapping under climate change in Bisha watershed, Saudi Arabia J. Mallick et al. 10.1080/10106049.2022.2088861
16. Ensemble Machine Learning Techniques for Accurate and Efficient Detection of Botnet Attacks in Connected Computers S. Afrifa et al. 10.3390/eng4010039
17. Integrating geological, hydrogeological and geophysical data to identify groundwater resources in granitic basement areas (Guéra Massif, Chad) H. Nouradine et al. 10.1007/s10040-024-02766-2
18. Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh S. Sarkar et al. 10.1007/s10668-024-04687-2
19. Determination of potential recharge zones and its validation against groundwater quality parameters through the application of GIS and remote sensing techniques in uMhlathuze catchment, KwaZulu-Natal, South Africa D. Ponnusamy & V. Elumalai 10.1016/j.chemosphere.2022.136121
20. Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets A. Jamil et al. 10.1016/j.jappgeo.2024.105493
21. MaxEnt machine learning model predicts high groundwater potential areas in a fractured volcanic aquifer system S. Ballardin et al. 10.1016/j.jsames.2024.104794
22. Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh S. Sarkar et al. 10.1038/s41598-024-60560-2
23. Predicting groundwater potential assessment in water-deficient islands based on convolutional neural networks H. Xu et al. 10.1016/j.ejrs.2022.11.002
24. A Machine Learning Approach to Map the Vulnerability of Groundwater Resources to Agricultural Contamination V. Gómez-Escalonilla & P. Martínez-Santos 10.3390/hydrology11090153
25. Developing meta-heuristic optimization based ensemble machine learning algorithms for hydraulic efficiency assessment of storm water grate inlets Ö. Ekmekcioğlu et al. 10.1080/1573062X.2022.2134806
26. Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modeling framework M. Habib & M. Hossain 10.1016/j.renene.2024.121258
27. A machine learning approach to site groundwater contamination monitoring wells V. Gómez-Escalonilla et al. 10.1007/s13201-024-02320-1
28. Delineating groundwater potential zones using geospatial techniques and fuzzy analytical hierarchy process (FAHP) ensemble in the data-scarce region: evidence from the lower Thoubal river watershed of Manipur, India M. Rahaman et al. 10.1007/s12517-022-09946-y
29. Identification of non-conventional groundwater resources by means of machine learning in the Aconcagua basin, Chile M. Aliaga-Alvarado et al. 10.1016/j.ejrh.2023.101502
30. Delineation of flood risk terrains and rainfall visualisation in the North Western part of Ghana B. Dekongmen et al. 10.1007/s40808-024-02041-z
31. A comparative and coupled study of the application of Dempster-Shafer, fuzzy overlay and FAHP methods for groundwater potential mapping in a fractured medium of a mine site M. Safari et al. 10.1007/s12145-023-01006-7
32. Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia T. Seifu et al. 10.2166/nh.2023.083
33. Sustainable groundwater development using semi-supervised learning and community-led total forestry and pasture approach U. S et al. 10.1016/j.gsd.2024.101093
34. Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin G. Víctor et al. 10.1080/10106049.2021.2007298