Physics-informed hybrid GR4J-XGBoost model for streamflow prediction: integrating conceptual states, SHAP interpretability, and uncertainty analysis.
- DOI
- 10.1038/s41598-026-64700-8
- Published
- 2026 Aug 3
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-64700-8,
title = {Physics-informed hybrid GR4J-XGBoost model for streamflow prediction: integrating conceptual states, SHAP interpretability, and uncertainty analysis.},
author = {Kisi O and Heddam S and Parmar KS and Külls C and Zounemat-Kermani M},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-64700-8},
url = {https://doi.org/10.1038/s41598-026-64700-8}
}RIS
TY - JOUR TI - Physics-informed hybrid GR4J-XGBoost model for streamflow prediction: integrating conceptual states, SHAP interpretability, and uncertainty analysis. AU - Kisi O AU - Heddam S AU - Parmar KS AU - Külls C AU - Zounemat-Kermani M PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-64700-8 UR - https://doi.org/10.1038/s41598-026-64700-8 ER -
APA
O, K., S, H., KS, P., C, K., & M, Z. (2026). Physics-informed hybrid GR4J-XGBoost model for streamflow prediction: integrating conceptual states, SHAP interpretability, and uncertainty analysis.. Scientific reports. https://doi.org/10.1038/s41598-026-64700-8
Source records
- pubmed · retrieved 2026-09-25T23:51:20.674Z