• artificial neural network;
  • bloom;
  • cyanobacteria;
  • multilayer perceptron;
  • prediction model;
  • self-organizing map

Cyanobacterial blooms are a common issue in eutrophic freshwaters, and some cyanobacteria produce toxins, threatening the health of humans and livestock. Microcystin, a representative cyanobacterial hepatotoxin, is frequently detected in most Korean lakes and reservoirs. This study developed predictive models for cyanobacterial bloom using artificial neural networks (ANNs; self-organizing map [SOM] and multilayer perceptron [MLP]), including an evaluation of related environmental factors. Fourteen environmental factors, as independent variables for predicting the cyanobacteria density, were measured weekly in the Daechung Reservoir from spring to autumn over 5 years (2001, 2003–2006). Cyanobacterial density was highly associated with environmental factors measured 3 weeks earlier. The SOM model was efficient in visualizing the relationships between cyanobacteria and environmental factors, and also for tracing temporal change patterns in the environmental condition of the reservoir. And the MLP model exhibited a good predictive power for the cyanobacterial density, based on the environmental factors of 3 weeks earlier. The water temperature and total dissolved nitrogen were the major determinants for cyanobacteria. The water temperature had a stronger influence on cyanobacterial growth than the nutrient concentrations in eutrophic waters. Contrary to general expectations, the nitrogen compounds played a more important role in bloom formation than the phosphorus compounds.