Source tracking of microbial intrusion in water systems using artificial neural networks

Authors

Minyoung Kim, University of Nebraska-LincolnFollow
Christopher Y. Choi, University of ArizonaFollow
Charles P. Garba, University of Arizona

Document Type

Article

Date of this Version

November 2007

Comments

Published in WATER RESEARCH.

Abstract

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 Escherichia coli using ANNs. Results showed that dispersion patterns of Escherichia coli were positively correlated to pH, turbidity, and conductivity (R² = 0.90–0.96), and the ANN models successfully identified the source location of Escherichia 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.