Multi-source eXplainable Artificial Intelligence for monitoring and interpretation of marine scrubber washwater quality from full-scale vessel data.
- DOI
- 10.1016/j.watres.2026.126722
- Published
- 2026-08-19
- Container
- Water Res
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.watres.2026.126722,
title = {Multi-source eXplainable Artificial Intelligence for monitoring and interpretation of marine scrubber washwater quality from full-scale vessel data.},
author = {Di Bonito LP and Campanile L and Iacono M and Della Ragione E and Di Natale F.},
year = {2026},
journal = {Water Res},
doi = {10.1016/j.watres.2026.126722},
url = {https://doi.org/10.1016/j.watres.2026.126722}
}RIS
TY - JOUR TI - Multi-source eXplainable Artificial Intelligence for monitoring and interpretation of marine scrubber washwater quality from full-scale vessel data. AU - Di Bonito LP AU - Campanile L AU - Iacono M AU - Della Ragione E AU - Di Natale F. PY - 2026 JO - Water Res DO - 10.1016/j.watres.2026.126722 UR - https://doi.org/10.1016/j.watres.2026.126722 ER -
APA
LP, D. B., L, C., M, I., E, D. R., & F., D. N. (2026). Multi-source eXplainable Artificial Intelligence for monitoring and interpretation of marine scrubber washwater quality from full-scale vessel data.. Water Res. https://doi.org/10.1016/j.watres.2026.126722
Source records
- europe-pmc · retrieved 2026-09-25T11:33:32.856Z