A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning- and Physics-Based Modeling Systems.
- DOI
- 10.1175/aies-d-22-0049.1
- Published
- 2023 Jul 1
- Container
- Artificial intelligence for the earth systems
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1175/aies-d-22-0049.1,
title = {A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning- and Physics-Based Modeling Systems.},
author = {Chang CF and Astitha M and Yuan Y and Tang C and Vlahos P and Garcia V and Khaira U},
year = {2023},
journal = {Artificial intelligence for the earth systems},
doi = {10.1175/aies-d-22-0049.1},
url = {https://doi.org/10.1175/aies-d-22-0049.1}
}RIS
TY - JOUR TI - A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning- and Physics-Based Modeling Systems. AU - Chang CF AU - Astitha M AU - Yuan Y AU - Tang C AU - Vlahos P AU - Garcia V AU - Khaira U PY - 2023 JO - Artificial intelligence for the earth systems DO - 10.1175/aies-d-22-0049.1 UR - https://doi.org/10.1175/aies-d-22-0049.1 ER -
APA
CF, C., M, A., Y, Y., C, T., P, V., V, G., & U, K. (2023). A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning- and Physics-Based Modeling Systems.. Artificial intelligence for the earth systems. https://doi.org/10.1175/aies-d-22-0049.1
Source records
- pubmed · retrieved 2026-09-26T17:59:05.926Z
- europe-pmc · retrieved 2026-09-26T17:59:05.948Z