Development of a decision-making system for use against the intrusion of biological agents into water systems

Minyoung Kim, Christopher Y. Choi, Charles P. Gerba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The feasibility of monitoring open-channel water systems as an early warning of the accidental or intentional release of biological agents was investigated. Critical steps in this study included (i) evaluation of the quantity of pathogens that would be released into the sewer system, (ii) how these organisms would be distributed in an open-channel system (accounting for dilution and dispersion), and (iii) how well they could be predicted at downstream locations. We developed and examined prediction models using computational tools such as CFD (Computational Fluid Dynamics) and ANNs (Artificial Neural Networks) for water collection systems though analyses of the collected data. The models were designed (i) to forecast microbial dispersion patterns in each system, (ii) to estimate dispersion time, and (iii) to recommend detection methods, sampling frequencies, and sampling locations. Based on a series of field experiments, those computational models which proved effective were designed to provide us with an impetus to establish an optimization technique for real-world situations. Field experiments and numerical simulation data were essential to evaluate the validity of the developed model. The use of ANNs for spatial and temporal identification of biological agents was conducted based on the particular characteristics resulting from pH, turbidity, and conductivity data corresponding to E. coli concentration over time. Overall, the simulation results for the two specific purposes of using ANNs, parameter estimation and feature classification, were highly satisfied (R2 = 0.77-0.96). It was concluded that ANNs could effectively be used for multiple tasks, such as prediction of the dispersion patterns of E. coli using its surrogates. In addition, various characteristics of the time-series concentration of E. coli, flow rate, inlet position, distance from an outlet, etc., were well considered in order to classify the release location and concentration.

Original languageEnglish (US)
Title of host publication31st IAHR Congress 2005
Subtitle of host publicationWater Engineering for the Future, Choices and Challenges
EditorsJun Byong-Ho, Il Lee Sang, Seo Il Won, Choi Gye-Woon
PublisherKorea Water Resources Association
Pages1077-1083
Number of pages7
ISBN (Electronic)8987898245, 9788987898247
StatePublished - 2005
Event31st IAHR Congress 2005: Water Engineering for the Future, Choices and Challenges - Seoul, Korea, Republic of
Duration: Sep 11 2005Sep 16 2005

Publication series

Name31st IAHR Congress 2005: Water Engineering for the Future, Choices and Challenges

Conference

Conference31st IAHR Congress 2005: Water Engineering for the Future, Choices and Challenges
CountryKorea, Republic of
CitySeoul
Period9/11/059/16/05

Keywords

  • Artificial neural networks
  • Biological agents
  • Decision making system
  • Feature classification
  • Generalized regression neural network
  • Microbial intrusion
  • Parameter estimation
  • Source identification
  • Water systems

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Environmental Science (miscellaneous)
  • Water Science and Technology

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