Predicting groundwater storage from seasonal managed aquifer recharge: insights from machine learning and explainable AI techniques

Valdrich J. Fernandes, Perry G. B. de Louw, Coen J. Ritsema, Ruud P. Bartholomeus

Open source

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

Credibility signals

uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

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