Interpretable machine learning characterizes climate-dependent associations between riverine phosphorus and anthropogenic-environmental factors across a large monitoring network.
- DOI
- 10.1016/j.jconhyd.2026.105123
- Published
- 2026 Sep 18
- Container
- Journal of contaminant hydrology
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jconhyd.2026.105123,
title = {Interpretable machine learning characterizes climate-dependent associations between riverine phosphorus and anthropogenic-environmental factors across a large monitoring network.},
author = {Sheikholeslami R and Vahab S and Nikoo MR},
year = {2026},
journal = {Journal of contaminant hydrology},
doi = {10.1016/j.jconhyd.2026.105123},
url = {https://doi.org/10.1016/j.jconhyd.2026.105123}
}RIS
TY - JOUR TI - Interpretable machine learning characterizes climate-dependent associations between riverine phosphorus and anthropogenic-environmental factors across a large monitoring network. AU - Sheikholeslami R AU - Vahab S AU - Nikoo MR PY - 2026 JO - Journal of contaminant hydrology DO - 10.1016/j.jconhyd.2026.105123 UR - https://doi.org/10.1016/j.jconhyd.2026.105123 ER -
APA
R, S., S, V., & MR, N. (2026). Interpretable machine learning characterizes climate-dependent associations between riverine phosphorus and anthropogenic-environmental factors across a large monitoring network.. Journal of contaminant hydrology. https://doi.org/10.1016/j.jconhyd.2026.105123
Source records
- pubmed · retrieved 2026-09-25T17:46:34.111Z