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

Data sets

Erken_Algal_Bloom_Machine_Learning_Model Shuqi Lin

Meteorological data from Erken, Malma island, 1988-10-12–2021-12-31 Erken Laboratory

Model code and software

Erken_Algal_Bloom_Machine_Learning_Model Shuqi Lin

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.