A neural network approach to identifying non-point sources of microbial contamination

Gail Montgomery Brion, Srinivasa Lingireddy

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


Commonly measured fecal bacteria concentrations in water and rainfall data were utilized as inputs for training a neural network model to distinguish between urban and agricultural fecal contamination present in inputs to a drinking water reservoir. Seven sites were selected that represented differing degrees of fecal contamination arising from agricultural, urban, or a blend of both land use activities. The absence of human sewage at the inlet sites to the reservoir was determined by analysis for coprostannol and serotyping of male-specific coliphage. Analyses for fecal coliform (FC), fecal streptococci (FS), total coliform (TC) and coliphage were conducted over 2 years from weekly samples collected from these sites during dry and rainy times during warm seasons. The average concentrations of microorganisms measured were highly variable and analysis of FC/FS ratios was not able to differentiate between urban or agriculturally impacted sites. A neural network model was written that used bacterial and weather data to differentiate between three site classifications: urban, agricultural and a mixture of these. The validity of the source identification, neural network model was verified through case study.

Original languageEnglish
Pages (from-to)3099-3106
Number of pages8
JournalWater Research
Issue number14
StatePublished - Oct 1999

Bibliographical note

Funding Information:
This research was partially supported by grants from the USGS via the Water Resource Research Institute of North Carolina, the Kentucky Water Resource Research Institute (KWRRI) and the Kentucky American Water Company (KAW). Besides financial support, we would like to recognize the efforts of the following individuals without whom this project could not have happened: Dillard Griffin, Mitzi Combs and Julie Simpson of KAW, Dr David White of the University of Tennessee, Dr Mark Sobsey of the University of North Carolina at Chapel Hill, Lyle Sendlein of KWRRI and Tonya McRay-Harding, Valerie Miller and Jon Herriford, University of Kentucky Civil Engineering graduate students responsible for data collection and analysis.


  • Drinking water
  • Fecal coliforms
  • Indicators
  • Modeling
  • Neural networks
  • Non-point sources
  • Runoff
  • Water quality
  • Watershed management

ASJC Scopus subject areas

  • Environmental Engineering
  • Civil and Structural Engineering
  • Ecological Modeling
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution


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