Case studies on the use of neural networks in eutrophication modeling

Karul C., Soyupak S., Cilesiz A., Akbay N., Germen E.

ECOLOGICAL MODELLING, vol.134, pp.145-152, 2000 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 134
  • Publication Date: 2000
  • Doi Number: 10.1016/s0304-3800(00)00360-4
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.145-152
  • Keywords: eutrophication, modeling, neural network, chlorophyll-a, KEBAN DAM-RESERVOIR, STRATEGIES
  • Middle East Technical University Affiliated: No


Artificial neural networks are becoming more and more common to be used in development of prediction models for complex systems as the theory behind them develops and the processing power of computers increase. A three layer Levenberg-Marquardt feedforward learning algorithm was used to model the eutrophication process in three water bodies of Turkey (Keban Dam Reservoir, Mogan and Eymir Lakes). Despite the very complex and peculiar nature of Keban Dam, a relatively good correlation (correlation coefficient between 0.60 and 0.75) was observed between the measured and calculated values. For Mogan and Eymir, which are much smaller and more homogenous lakes compared to Keban Dam Reservoir, correlation values as high as 0.95 were achieved between the measured and calculated values. Neural network models were able to model non-linear behavior in eutrophication process reasonably well and could successfully estimate some extreme values from validation and test data sets which were not used in training the neural network. (C) 2000 Elsevier Science B.V. All rights reserved.