Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-35-2023
https://doi.org/10.5194/gmd-16-35-2023
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

Viewed

Total article views: 3,114 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,248 787 79 3,114 187 72 63
  • HTML: 2,248
  • PDF: 787
  • XML: 79
  • Total: 3,114
  • Supplement: 187
  • BibTeX: 72
  • EndNote: 63
Views and downloads (calculated since 02 Aug 2022)
Cumulative views and downloads (calculated since 02 Aug 2022)

Viewed (geographical distribution)

Total article views: 3,114 (including HTML, PDF, and XML) Thereof 3,044 with geography defined and 70 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
Download
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.