A neural-network-based classification scheme for sorting sources and ages of fecal contamination in water.
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.
D000383:Agriculture; D000818:Animals; D000829:Animals, Domestic; D001419:Bacteria; D004784:Environmental Monitoring; D005243:Feces; D005544:Forecasting; D016571:Neural Networks, Computer; D010316:Particle Size; D014873:Water Pollutants / Q000145:classification*; D014887:Weather
D014873:Water Pollutants