A neural-network-based classification scheme for sorting sources and ages of fecal contamination in water

Gail M. Brion, T. R. Neelakantan, Srinivasa Lingireddy

Research output: Contribution to journalArticlepeer-review

39 Citations (SciVal)

Abstract

Artificial neural networks (ANNs) were successfully applied to data observations from a small watershed consisting of commonly measured indicator bacteria, weather conditions, and turbidity to distinguish between human sewage and animal-impacted runoff, fresh runoff from aged, and agricultural land-use-associated fresh runoff from that of suburban land-use-associated-fresh runoff. The ANNs were applied in a cascading, or hierarchical scheme. ANN performance was measured in two ways: (1) training and (2) testing. An ANN was able to sort sewage from runoff with <1% error. Turbidity was found to be relatively unimportant for sorting sewage from runoff, while gross measurements of gram-negative and gram-positive bacteria were required. Predictions clustered tightly around the known values. ANN classification of aged suburban runoff from fresh, and agricultural runoff from suburban was accomplished with >90% accuracy.

Original languageEnglish
Pages (from-to)3765-3774
Number of pages10
JournalWater Research
Volume36
Issue number15
DOIs
StatePublished - Sep 2002

Keywords

  • Fecal indicators
  • Land-use
  • Neural networks
  • Runoff
  • Sewage

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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