Use of artificial neural networks to evaluate the effectiveness of riverbank filtration

Goloka B. Sahoo, Chittaranjan Ray, Jack Z. Wang, Stephen A. Hubbs, Rengao Song, Jay Jasperse, Donald Seymour

Research output: Contribution to journalArticlepeer-review


Riverbank filtration (RBF) is a low-cost water treatment technology in which surface water contaminants are removed or degraded as the infiltrating water moves from the river/lake to the pumping wells. The removal or degradation of contaminants is a combination of physicochemical and biological processes. This paper illustrates the development and application of three types of artificial neural networks (ANNs) to estimate the effectiveness of two RBF facilities in the US. The feed-forward back-propagation network (BPN) and radial basis function network (RBFN) model prediction results produced excellent agreement with measured data at a correlation coefficient above 0.99 for filtrate water quality parameters, including temperature as well as turbidity, heterotrophic bacteria, and coliform removal. In comparison, the fuzzy inference system network (FISN) predicted only temperature and bacteria removal with reasonable accuracy. It is shown that the predictive performances of the ANNs depend on the model structure and model inputs.

Original languageEnglish (US)
Pages (from-to)2505-2516
Number of pages12
JournalWater Research
Issue number12
StatePublished - Jul 2005
Externally publishedYes


  • Back propagation
  • Neural fuzzy model
  • Radial basis function
  • Riverbank filtration
  • Water quality

ASJC Scopus subject areas

  • Ecological Modeling
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution


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