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
Anna Pazola
CORRESPONDING AUTHOR
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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Cited
13 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. 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 10.3390/w17081121
- 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. 10.3390/rs17081460
- 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. 10.1016/j.apgeochem.2025.106285
- Predicting piezometric levels in the Araripe Sedimentary Basin, Brazil, using regression and machine learning models M. Lima et al. 10.1007/s10040-025-02941-z
- Hydrology and Climate Change in Africa: Contemporary Challenges, and Future Resilience Pathways O. Adeyeri 10.3390/w17152247
- Predicting groundwater withdrawals using machine learning with limited metering data: Assessment of training data requirements D. Asfaw et al. 10.1016/j.agwat.2025.109691
- Water, Ecosystem Services, and Urban Green Spaces in the Anthropocene M. Olivadese & M. Dindo 10.3390/land13111948
- 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. 10.1016/j.gsd.2025.101503
- Unveiling the escalating impact of human activities on groundwater storage in ecologically fragile steppe, Northern China X. Zhang et al. 10.1016/j.jhydrol.2025.133296
- Rising threats to groundwater recharge: Adaptive strategies for the Sahel under climate change F. Granata & F. Di Nunno 10.1016/j.gsd.2025.101468
- Enhancing Groundwater Recharge Prediction: A Feature Selection‐Based Deep Forest Model With Bayesian Optimisation B. Liu et al. 10.1002/hyp.15309
- High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation A. Pazola et al. 10.5194/hess-28-2949-2024
11 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. 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 10.3390/w17081121
- 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. 10.3390/rs17081460
- 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. 10.1016/j.apgeochem.2025.106285
- Predicting piezometric levels in the Araripe Sedimentary Basin, Brazil, using regression and machine learning models M. Lima et al. 10.1007/s10040-025-02941-z
- Hydrology and Climate Change in Africa: Contemporary Challenges, and Future Resilience Pathways O. Adeyeri 10.3390/w17152247
- Predicting groundwater withdrawals using machine learning with limited metering data: Assessment of training data requirements D. Asfaw et al. 10.1016/j.agwat.2025.109691
- Water, Ecosystem Services, and Urban Green Spaces in the Anthropocene M. Olivadese & M. Dindo 10.3390/land13111948
- 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. 10.1016/j.gsd.2025.101503
- Unveiling the escalating impact of human activities on groundwater storage in ecologically fragile steppe, Northern China X. Zhang et al. 10.1016/j.jhydrol.2025.133296
- Rising threats to groundwater recharge: Adaptive strategies for the Sahel under climate change F. Granata & F. Di Nunno 10.1016/j.gsd.2025.101468
2 citations as recorded by crossref.
- Enhancing Groundwater Recharge Prediction: A Feature Selection‐Based Deep Forest Model With Bayesian Optimisation B. Liu et al. 10.1002/hyp.15309
- High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation A. Pazola et al. 10.5194/hess-28-2949-2024
Latest update: 28 Aug 2025
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...
