Articles | Volume 28, issue 13
https://doi.org/10.5194/hess-28-2949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jul 2024
Research article |
| 05 Jul 2024
| 05 Jul 2024
High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation
Department of Geography, University College London, Gower St., London, WC1E 6BT, United Kingdom
Department of Computer Science, Brunel University London, Kingston Lane, Uxbridge, UB8 3PH, United Kingdom
Mohammad Shamsudduha
Department of Risk and Disaster Reduction, University College London, Gower St., London, WC1E 6BT, United Kingdom
Jon French
Department of Geography, University College London, Gower St., London, WC1E 6BT, United Kingdom
Alan M. MacDonald
British Geological Survey, Lyell Centre, Research Avenue South, Edinburgh, EH14 4AP, United Kingdom
Tamiru Abiye
School of Geosciences, University of the Witwatersrand, 1 Jan Smuts Ave, Braamfontein, 2000 Johannesburg, South Africa
Ibrahim Baba Goni
Department of Geology, University of Maiduguri, 1069 Bama, Maiduguri Rd, 600104 Maiduguri, Nigeria
Richard G. Taylor
Department of Geography, University College London, Gower St., London, WC1E 6BT, United Kingdom
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26 citations as recorded by crossref.
- Geoinformation and Analytical Support for the Development of Promising Aquifers for Pasture Water Supply in Southern Kazakhstan S. Tazhiyev et al. https://doi.org/10.3390/w17091297
- Understanding Terrestrial Water Storage Changes Derived from the GRACE/GRACE-FO in the Inner Niger Delta in West Africa F. Fatolazadeh & K. Goïta https://doi.org/10.3390/w17081121
- Predicting groundwater withdrawals using machine learning with limited metering data: Assessment of training data requirements D. Asfaw et al. https://doi.org/10.1016/j.agwat.2025.109691
- Water, Ecosystem Services, and Urban Green Spaces in the Anthropocene M. Olivadese & M. Dindo https://doi.org/10.3390/land13111948
- Comment on “On-site sanitation systems and fecal contamination in shallow groundwater in urban Indonesia: assessing influence of distance and rainfall variables” By G. L. Putri, A. M. Ilmi, R. Handayani, D. Iskandar, C. R. Priadi, J. Willetts, T. Foster; Water Research Volume 287 (Part B), 20 August 2025, 124431 H. Yin et al. https://doi.org/10.1016/j.watres.2025.125214
- Unveiling the escalating impact of human activities on groundwater storage in ecologically fragile steppe, Northern China X. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133296
- Mapping deforestation probability and understanding the forest dynamics in Gazipur, Bangladesh P. Chakraborty & P. Talukder https://doi.org/10.1016/j.envc.2026.101568
- Determination of precipitation infiltration recharge coefficient based on multi-factor integration in the Huaibei Plain Region of Anhui Province, China H. Lei et al. https://doi.org/10.1080/27678490.2026.2656244
- Hydrology and Climate Change in Africa: Contemporary Challenges, and Future Resilience Pathways O. Adeyeri https://doi.org/10.3390/w17152247
- Enhancing groundwater level prediction through DeepMVI‑Aided interpolation and transformer‑based modeling A. Vafaei et al. https://doi.org/10.1007/s12145-026-02153-3
- Long-term and interannual variability of African terrestrial water storage revealed by GRACE/GRACE-FO and models G. Xu et al. https://doi.org/10.1016/j.jhydrol.2026.135794
- A Summary of Recent Advances in the Literature on Machine Learning Techniques for Remote Sensing of Groundwater Dependent Ecosystems (GDEs) from Space C. Chiloane et al. https://doi.org/10.3390/rs17081460
- Large-scale modeling of solar water pumps using machine learning G. Zuffinetti et al. https://doi.org/10.1016/j.apenergy.2025.127268
- Field-based explainable machine learning for interval-scale groundwater dynamics in agricultural managed aquifer recharge (Ag-MAR) M. Eltarabily et al. https://doi.org/10.1007/s00477-026-03282-3
- Delineation of groundwater potential zones in hard rock terrains using geospatial techniques and AHP method: A case study from Notse, southern Togo, West Africa K. Agbotsou et al. https://doi.org/10.1016/j.gsd.2025.101503
- Fires enhanced productivity in fire-adapted subtropical pinelands of the Florida Everglades G. McLeod et al. https://doi.org/10.1016/j.scitotenv.2025.180602
- Machine learning-based modeling of groundwater recharge under three climate change scenarios in the Densu Basin of Ghana, West Africa G. Jesse et al. https://doi.org/10.1016/j.gsd.2026.101611
- Comparative analysis of AHP, MIF, and FR methods for sustainable groundwater management in Togo, West Africa K. Agbotsou et al. https://doi.org/10.1016/j.oreoa.2026.100137
- Coal–Rock Damage and Subsidence Control Technology in Deep‐Mine Strip Mining Under Large Mining Heights J. Shuyin et al. https://doi.org/10.1155/gfl/6041445
- Predicting salinity and alkalinity fluxes of U.S. freshwater in a changing climate: Integrating anthropogenic and natural influences using data-driven models B. E et al. https://doi.org/10.1016/j.apgeochem.2025.106285
- High-resolution hydrological modeling to assess groundwater recharge dynamics in the Colombian Orinoco O. Rueda-Franco et al. https://doi.org/10.1016/j.ejrh.2025.103066
- Machine Learning–Based Characterization of Groundwater Recharge in Semi-Arid Drylands S. Azghandi & E. Behnamtalab https://doi.org/10.1007/s11269-026-04557-8
- Predicting piezometric levels in the Araripe Sedimentary Basin, Brazil, using regression and machine learning models M. Lima et al. https://doi.org/10.1007/s10040-025-02941-z
- Water‐Inrush Risk Assessment of Roof‐Confined Aquifer Strata Based on FAHP–RF Model in the Arid–Semiarid Ecotone: A Case Study of Zhuanlongwan Coal Mine S. Xue et al. https://doi.org/10.1155/gfl/5360550
- Rising threats to groundwater recharge: Adaptive strategies for the Sahel under climate change F. Granata & F. Di Nunno https://doi.org/10.1016/j.gsd.2025.101468
- Spatio-temporal analysis of surface and groundwater interactions in the Amboni River Basin, Kenya, using SWAT-MODFLOW J. Wanjala et al. https://doi.org/10.1007/s40899-025-01309-1
Saved (final revised paper)
Latest update: 06 Aug 2026
Short summary
This study advances groundwater research using a high-resolution random forest model, revealing new recharge areas and spatial variability, mainly in humid regions. Limited data in rainy zones is a constraint for the model. Our findings underscore the promise of machine learning for large-scale groundwater modelling while further emphasizing the importance of data collection for robust results.
This study advances groundwater research using a high-resolution random forest model, revealing...
