Synthetic experience augmentation: A two-regime framework for extending LSTM extrapolation in rapid sand filter clogging prediction under water utility consolidation.
- DOI
- 10.1016/j.watres.2026.127196
- Published
- 2026 Oct 23
- Container
- Water research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.watres.2026.127196,
title = {Synthetic experience augmentation: A two-regime framework for extending LSTM extrapolation in rapid sand filter clogging prediction under water utility consolidation.},
author = {Yamamura H and Nakane K and Shimaya S and Kiyozuka M},
year = {2026},
journal = {Water research},
doi = {10.1016/j.watres.2026.127196},
url = {https://doi.org/10.1016/j.watres.2026.127196}
}RIS
TY - JOUR TI - Synthetic experience augmentation: A two-regime framework for extending LSTM extrapolation in rapid sand filter clogging prediction under water utility consolidation. AU - Yamamura H AU - Nakane K AU - Shimaya S AU - Kiyozuka M PY - 2026 JO - Water research DO - 10.1016/j.watres.2026.127196 UR - https://doi.org/10.1016/j.watres.2026.127196 ER -
APA
H, Y., K, N., S, S., & M, K. (2026). Synthetic experience augmentation: A two-regime framework for extending LSTM extrapolation in rapid sand filter clogging prediction under water utility consolidation.. Water research. https://doi.org/10.1016/j.watres.2026.127196
Source records
- pubmed · retrieved 2026-11-10T21:30:49.592Z