Feature-engineered machine learning for daily-scale prediction of effluent total phosphorus and coagulant dosing optimization in full-scale DAF systems.
- DOI
- 10.2166/wst.2026.256
- Published
- 2026 Apr
- Container
- Water science and technology : a journal of the International Association on Water Pollution Research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.2166/wst.2026.256,
title = {Feature-engineered machine learning for daily-scale prediction of effluent total phosphorus and coagulant dosing optimization in full-scale DAF systems.},
author = {Park H and Jeong G and Oh Y and Kim T and Kim M and Koo J},
year = {2026},
journal = {Water science and technology : a journal of the International Association on Water Pollution Research},
doi = {10.2166/wst.2026.256},
url = {https://doi.org/10.2166/wst.2026.256}
}RIS
TY - JOUR TI - Feature-engineered machine learning for daily-scale prediction of effluent total phosphorus and coagulant dosing optimization in full-scale DAF systems. AU - Park H AU - Jeong G AU - Oh Y AU - Kim T AU - Kim M AU - Koo J PY - 2026 JO - Water science and technology : a journal of the International Association on Water Pollution Research DO - 10.2166/wst.2026.256 UR - https://doi.org/10.2166/wst.2026.256 ER -
APA
H, P., G, J., Y, O., T, K., M, K., & J, K. (2026). Feature-engineered machine learning for daily-scale prediction of effluent total phosphorus and coagulant dosing optimization in full-scale DAF systems.. Water science and technology : a journal of the International Association on Water Pollution Research. https://doi.org/10.2166/wst.2026.256
Source records
- pubmed · retrieved 2026-09-25T12:18:23.632Z