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

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

doi:https://doi.org/10.5194/gmd-16-35-2023

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

Supplement

https://doi.org/10.5194/gmd-16-35-2023-supplement

Data sets

Erken_Algal_Bloom_Machine_Learning_Model Shuqi Lin https://doi.org/10.5281/zenodo.7149563

Meteorological data from Erken, Malma island, 1988-10-12–2021-12-31 Erken Laboratory https://hdl.handle.net/11676.1/qZYc4CMTOyxgvjv_gTAW08SO

Model code and software

Erken_Algal_Bloom_Machine_Learning_Model Shuqi Lin https://doi.org/10.5281/zenodo.7149563

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.