An explainable and causally informed machine learning framework for hydrological drought transitions, propagation, and vulnerability.
- DOI
- 10.1016/j.watres.2026.127052
- Published
- 2026 Oct 1
- Container
- Water research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.watres.2026.127052,
title = {An explainable and causally informed machine learning framework for hydrological drought transitions, propagation, and vulnerability.},
author = {Leng J and Ma K and Quang NH and Luo Y and Li Y and Cao J and He D},
year = {2026},
journal = {Water research},
doi = {10.1016/j.watres.2026.127052},
url = {https://doi.org/10.1016/j.watres.2026.127052}
}RIS
TY - JOUR TI - An explainable and causally informed machine learning framework for hydrological drought transitions, propagation, and vulnerability. AU - Leng J AU - Ma K AU - Quang NH AU - Luo Y AU - Li Y AU - Cao J AU - He D PY - 2026 JO - Water research DO - 10.1016/j.watres.2026.127052 UR - https://doi.org/10.1016/j.watres.2026.127052 ER -
APA
J, L., K, M., NH, Q., Y, L., Y, L., J, C., & D, H. (2026). An explainable and causally informed machine learning framework for hydrological drought transitions, propagation, and vulnerability.. Water research. https://doi.org/10.1016/j.watres.2026.127052
Source records
- pubmed · retrieved 2026-11-10T12:33:33.966Z