Source tracking of microbial intrusion in water systems using artificial neural networks.
A "what-if" scenario where biological agents are accidentally or deliberately introduced into a water system was generated, and artificial neural network (ANN) models were applied to identify the pathogenic release location to isolate the contaminated area and minimize its hazards. The spatiotemporal distribution of Escherichia coli 15597 along the water system was employed to locate pollutants by inversely interpreting transport patterns of E. coli using ANNs. Results showed that dispersion patterns of E. coli were positively correlated to pH, turbidity, and conductivity (R2=0.90-0.96), and the ANN models successfully identified the source location of E. coli introduced into a given system with 75% accuracy based on the pre-programmed relationships between E. coli transport patterns and release locations. The findings in this study will enable us to assess the vulnerability of essential water systems, establish the early warning system and protect humans and the environment.
Kim
Minyoung
M
Agroecosystem Management Research Unit, USDA-ARS, 120 Keim Hall, East Campus, University of Nebraska, Lincoln, NE 68583, USA. Minyoung.Kim@ars.usda.gov <Minyoung.Kim@ars.usda.gov>
Choi
Christopher Y
CY
Gerba
Charles P
CP
D004784:Environmental Monitoring / Q000379:methods*; D004926:Escherichia coli / Q000302:isolation & purification*; D016571:Neural Networks, Computer; D014871:Water Microbiology; D014872:Water Movements; D014873:Water Pollutants / Q000302:isolation & purification*
D014873:Water Pollutants