49 citations as recorded by crossref.

1. Prediction of nitrate concentration and the impact of land use types on groundwater in the Nansi Lake Basin J. Iqbal et al.
2. Groundwater potential mapping in arid and semi-arid regions of kurdistan region of Iraq: A geoinformatics-based machine learning approach K. Fatah et al.
3. Utilisation of Agro-Wastes as Adsorbents for Fluoride and Co-Existing Ions Adsorption from Groundwater Sourced from Bongo, Ghana I. Quaicoe et al.
4. 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
5. Potential of machine learning algorithms in groundwater level prediction using temporal gravity data H. Sarkar et al.
6. AI-driven prediction of carbonation depth in recycled aggregate concrete: Influential parameters and nonlinear interactions from machine learning perspectives Y. Abbas et al.
7. Harnessing machine learning for assessing climate change influences on groundwater resources: A comprehensive review A. Bamal et al.
8. Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms X. Guo et al.
9. 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. Delineation of Groundwater Potential Using the Bivariate Statistical Models and Hybridized Multi-Criteria Decision-Making Models M. Baduna Koçyiğit & H. Akay
11. Developing a new method for future groundwater potentiality mapping under climate change in Bisha watershed, Saudi Arabia J. Mallick et al.
12. Ensemble Machine Learning Techniques for Accurate and Efficient Detection of Botnet Attacks in Connected Computers S. Afrifa et al.
13. 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.
14. A surrogate approach to model groundwater level in time and space based on tree regressors P. Martínez-Santos et al.
15. Assessment of groundwater development in watersheds using novel hydrosociological indicators and machine learning approach R. Eliza et al.
16. A Machine Learning Approach to Map the Vulnerability of Groundwater Resources to Agricultural Contamination V. Gómez-Escalonilla & P. Martínez-Santos
17. Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modeling framework M. Habib & M. Hossain
18. A machine learning approach to site groundwater contamination monitoring wells V. Gómez-Escalonilla et al.
19. Approach to mapping groundwater-dependent ecosystems through machine learning in central Chile I. Duran-Llacer et al.
20. Spatial prediction of groundwater potential by various novel boosting-based ensemble learning models in mountainous areas H. Xiong et al.
21. Groundwater potential mapping in endorheic basins using remote sensing and ensemble learning algorithms: A case study of the Bahira aquifer, Morocco F. Ibna et al.
22. Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables H. Touré et al.
23. 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.
24. Integrated machine learning and remote sensing for groundwater potential mapping in the Mekong Delta in Vietnam H. Nguyen et al.
25. Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco L. Ouali et al.
26. Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learning K. Dahal et al.
27. Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping S. Razavi-Termeh et al.
28. Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers . Gómez-Escalonilla et al.
29. Groundwater potential mapping of the central region using integrated geological and geophysical methods H. Touré et al.
30. Machine and deep learning in geological applications: a review of advances, challenges, and future research directions N. Hammouri et al.
31. Integrating GIS, Remote Sensing, and Machine Learning to Optimize Sustainable Groundwater Recharge in Arid Mediterranean Landscapes: A Case Study from the Middle Draa Valley, Morocco A. Moumane et al.
32. Integrating geological, hydrogeological and geophysical data to identify groundwater resources in granitic basement areas (Guéra Massif, Chad) H. Nouradine et al.
33. 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
34. Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets A. Jamil et al.
35. HydroPredictor a hybrid machine learning model for addressing data scarcity in groundwater prediction A. Elmotawakkil et al.
36. MaxEnt machine learning model predicts high groundwater potential areas in a fractured volcanic aquifer system S. Ballardin et al.
37. Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh S. Sarkar et al.
38. Predicting groundwater potential assessment in water-deficient islands based on convolutional neural networks H. Xu et al.
39. Comparative analysis of groundwater potential zones using machine learning and hybrid method in Nelson Mandela Bay Metropolitan Municipality, South Africa I. Shandu et al.
40. Developing meta-heuristic optimization based ensemble machine learning algorithms for hydraulic efficiency assessment of storm water grate inlets Ö. Ekmekcioğlu et al.
41. Development of a web-based No coding machine learning platform for hydrology and environmental management - MoolML S. Bak et al.
42. 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.
43. Identification of non-conventional groundwater resources by means of machine learning in the Aconcagua basin, Chile M. Aliaga-Alvarado et al.
44. Delineation of flood risk terrains and rainfall visualisation in the North Western part of Ghana B. Dekongmen et al.
45. 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.
46. Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia T. Seifu et al.
47. Estimation of soil temperature for agricultural applications in South Africa using machine-learning methods L. Myeni et al.
48. Groundwater Potential Mapping Using Machine Learning Techniques: Current Trends and Future Perspectives M. Ali et al.
49. Sustainable groundwater development using semi-supervised learning and community-led total forestry and pasture approach U. S et al.