Evaluation of environmental factors on cyanobacterial bloom in eutrophic reservoir using artificial neural networks

Cited 27 time in scopus
Metadata Downloads
Evaluation of environmental factors on cyanobacterial bloom in eutrophic reservoir using artificial neural networks
Chi-Yong AhnHee-Mock Oh; Y S Park
Bibliographic Citation
Journal of Phycology, vol. 47, no. 3, pp. 495-504
Publication Year
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 5years (2001, 2003-2006). Cyanobacterial density was highly associated with environmental factors measured 3weeks 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 3weeks 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.
Artificial neural networkBloomCyanobacteriaMultilayer perceptronPrediction modelSelf-organizing map
Appears in Collections:
Synthetic Biology and Bioengineering Research Institute > Cell Factory Research Center > 1. Journal Articles
Files in This Item:
  • There are no files associated with this item.

Items in OpenAccess@KRIBB are protected by copyright, with all rights reserved, unless otherwise indicated.