AI-driven risk management for sustainable water distribution: A comparative study of resampling strategies and cost-sensitive predictive modeling for leakage failure.
- DOI
- 10.1016/j.watres.2026.126622
- Published
- 2026 Aug 7
- Container
- Water research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.watres.2026.126622,
title = {AI-driven risk management for sustainable water distribution: A comparative study of resampling strategies and cost-sensitive predictive modeling for leakage failure.},
author = {Abert-Fernández D and Monclús H and Fetai B and Kozelj D},
year = {2026},
journal = {Water research},
doi = {10.1016/j.watres.2026.126622},
url = {https://doi.org/10.1016/j.watres.2026.126622}
}RIS
TY - JOUR TI - AI-driven risk management for sustainable water distribution: A comparative study of resampling strategies and cost-sensitive predictive modeling for leakage failure. AU - Abert-Fernández D AU - Monclús H AU - Fetai B AU - Kozelj D PY - 2026 JO - Water research DO - 10.1016/j.watres.2026.126622 UR - https://doi.org/10.1016/j.watres.2026.126622 ER -
APA
D, A., H, M., B, F., & D, K. (2026). AI-driven risk management for sustainable water distribution: A comparative study of resampling strategies and cost-sensitive predictive modeling for leakage failure.. Water research. https://doi.org/10.1016/j.watres.2026.126622
Source records
- pubmed · retrieved 2026-09-25T11:59:14.664Z