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

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 differi...

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Bibliographic Details
Published in:Water research (Oxford) 1999, Vol.33 (14), p.3099-3106
Main Authors: Brion, Gail Montgomery, Lingireddy, Srinivasa
Format: Article
Language:English
Subjects:
agricultural runoff
Applied sciences
Coliform bacteria
Continental surface waters
drinking water
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
Exact sciences and technology
fecal coliforms
fecal flora
identification
indicators
Land use
microbial contamination
modeling
Natural water pollution
Neural networks
non-point sources
Pollution
Pollution, environment geology
Potable water
reservoirs
Reservoirs (water)
Runoff
urban runoff
Water analysis
Water bacteriology
water pollution
Water quality
Water treatment and pollution
watershed management
Watersheds
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Summary: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.
ISSN:0043-1354
1879-2448
DOI:10.1016/S0043-1354(99)00025-1