25 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. 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
3. 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
4. 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
5. Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learning K. Dahal et al. 10.1016/j.jhydrol.2023.130417
6. 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
7. Potential of machine learning algorithms in groundwater level prediction using temporal gravity data H. Sarkar et al. 10.1016/j.gsd.2024.101114
8. 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
9. 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
10. 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
11. Ensemble Machine Learning Techniques for Accurate and Efficient Detection of Botnet Attacks in Connected Computers S. Afrifa et al. 10.3390/eng4010039
12. 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
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. 10.1007/s10668-024-04687-2
14. 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
15. 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
16. Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh S. Sarkar et al. 10.1038/s41598-024-60560-2
17. Predicting groundwater potential assessment in water-deficient islands based on convolutional neural networks H. Xu et al. 10.1016/j.ejrs.2022.11.002
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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