Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 1
Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-35-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-35-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 1
Model description paper
 | 
03 Jan 2023
Model description paper |  | 03 Jan 2023

Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake

Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman

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Latest update: 24 Apr 2024
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Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
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