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Using machine learning to understand the implications of meteorological conditions for fish kills

Chen, You‑Jia, Nicholson, Emily and Cheng, You‑Jia 2020, Using machine learning to understand the implications of meteorological conditions for fish kills, Scientific Reports, vol. 10, no. 1, pp. 1-13, doi: 10.1038/s41598-020-73922-3.

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Title Using machine learning to understand the implications of meteorological conditions for fish kills
Author(s) Chen, You‑Jia
Nicholson, EmilyORCID iD for Nicholson, Emily orcid.org/0000-0003-2199-3446
Cheng, You‑Jia
Journal name Scientific Reports
Volume number 10
Issue number 1
Article ID 17003
Start page 1
End page 13
Total pages 13
Publisher Springer Science and Business Media LLC
Place of publication London, Eng.
Publication date 2020
ISSN 2045-2322
2045-2322
Summary Fish kills, often caused by low levels of dissolved oxygen (DO), involve with complex interactions and dynamics in the environment. In many places the precise cause of massive fish kills remains uncertain due to a lack of continuous water quality monitoring. In this study, we tested if meteorological conditions could act as a proxy for low levels of DO by relating readily available meteorological data to fish kills of grey mullet Mugil cephalus using a machine learning technique, the self-organizing map (SOM). Driven by different meteorological patterns, fish kills were classified into summer and non-summer types by the SOM. Summer fish kills were associated with extended periods of lower air pressure and higher temperature, and concentrated storm events 2–3 days before the fish kills. In contrast, non-summer fish kills followed a combination of relatively low air pressure, continuous lower wind speed, and successive storm events 5 days before the fish kills. Our findings suggest that abnormal meteorological conditions can serve as warning signals for managers to avoid fish kills by taking preventative actions. While not replacing water monitoring programs, meteorological data can support fishery management to safeguard the health of the riverine ecosystems
Language eng
DOI 10.1038/s41598-020-73922-3
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30144437

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.