Predicting groundwater storage from seasonal managed aquifer recharge: insights from machine learning and explainable AI techniques
- DOI
- 10.1007/s12665-026-12825-4
- Published
- 2026-02-18
- Container
- Environmental Earth Sciences
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s12665-026-12825-4,
title = {Predicting groundwater storage from seasonal managed aquifer recharge: insights from machine learning and explainable AI techniques},
author = {Valdrich J. Fernandes and Perry G. B. de Louw and Coen J. Ritsema and Ruud P. Bartholomeus},
year = {2026},
journal = {Environmental Earth Sciences},
doi = {10.1007/s12665-026-12825-4},
url = {https://doi.org/10.1007/s12665-026-12825-4}
}RIS
TY - JOUR TI - Predicting groundwater storage from seasonal managed aquifer recharge: insights from machine learning and explainable AI techniques AU - Valdrich J. Fernandes AU - Perry G. B. de Louw AU - Coen J. Ritsema AU - Ruud P. Bartholomeus PY - 2026 JO - Environmental Earth Sciences DO - 10.1007/s12665-026-12825-4 UR - https://doi.org/10.1007/s12665-026-12825-4 ER -
APA
Fernandes, V. J., Louw, P. G. B. D., Ritsema, C. J., & Bartholomeus, R. P. (2026). Predicting groundwater storage from seasonal managed aquifer recharge: insights from machine learning and explainable AI techniques. Environmental Earth Sciences. https://doi.org/10.1007/s12665-026-12825-4
Source records
- crossref · retrieved 2026-09-26T19:15:26.752Z