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.

[1]  Brad Warner,et al.  Understanding Neural Networks as Statistical Tools , 1996 .

[2]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[3]  Y. Chtioui,et al.  A generalized regression neural network and its application for leaf wetness prediction to forecast plant disease , 1999 .

[4]  A. E. Greenberg,et al.  Standard methods for the examination of water and wastewater : supplement to the sixteenth edition , 1988 .

[5]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[6]  William P. Ball,et al.  Application of inverse methods to contaminant source identification from aquitard diffusion profiles at Dover AFB, Delaware , 1999 .

[7]  H. White,et al.  There exists a neural network that does not make avoidable mistakes , 1988, IEEE 1988 International Conference on Neural Networks.

[8]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[9]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[10]  B. Irie,et al.  Capabilities of three-layered perceptrons , 1988, IEEE 1988 International Conference on Neural Networks.

[12]  W. M. Mac Kenzie,et al.  How safe is our drinking water? , 2000, Postgraduate medicine.

[13]  T. van Kalken,et al.  Risk analyses for sewer systems based on numerical modelling and GIS , 1998 .