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Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater
Rad, Mehran
Abtahi, Azra
Berndtsson, Ronny
ORCID iD:
0000-0003-1473-0138
McKnight, Ursula
SMHI, Research Department, Hydrology.
ORCID iD:
0000-0001-8363-8672
Aminifar, Amir
Show others and affiliations
2024 (English)
In:
Environmental Pollution, ISSN 0269-7491, E-ISSN 1873-6424, Vol. 345, article id 123449
Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2024. Vol. 345, article id 123449
National Category
Oceanography, Hydrology and Water Resources
Research subject
Hydrology
Identifiers
URN:
urn:nbn:se:smhi:diva-6597
DOI:
10.1016/j.envpol.2024.123449
ISI:
001175351200001
PubMedID:
38278404
OAI: oai:DiVA.org:smhi-6597
DiVA, id:
diva2:1848211
Available from:
2024-04-02
Created:
2024-04-02
Last updated:
2024-04-02
Bibliographically approved
Open Access in DiVA
Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater
(12823 kB)
47 downloads
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36813614faf6b53e21fda1f2635348758b91b51c7e5f4193bb3ef8e07c8f37149c291473dd0f14f5e293f4a6181c3c96d1dec1e305b06f972796c8ccafca8c57
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McKnight, Ursula
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