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 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.