Assessment of physically-based and data-driven models to predict microbial water quality in open channels

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

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

In the present study, a physically-based hydraulic modeling tool and a data-driven approach using artificial neural networks (ANNs) were evaluated for their ability to simulate the fate and transport of microorganisms in a water system. To produce reliable data, a pipe network was constructed and a series of experiments using a fecal coliform indicator (Escherichia coli 15597) was conducted. For the physically-based model, morphological (pipe size, link length, slope, etc.) and hydraulic (flow rate) conditions were used as input variables, and for ANNs, water quality parameters (conductivity, pH, and turbidity) were used. Both approaches accurately described the fate and transport of microorganisms (physically-based model: correlation coeffcient (R) in the range of 0.914 - 0.977 and ANNs: R in the range of 0.949 - 0.980), with the exception of one case at a low flow rate (q = 31.56 cm3/sec). This study also indicated that these approaches could be complementarily utilized to assess the vulnerability of water facilities and to establish emergency plans based on hypothetical scenarios.

Original languageEnglish (US)
Pages (from-to)851-857
Number of pages7
JournalJournal of Environmental Sciences
Volume22
Issue number6
DOIs
StatePublished - Jun 24 2010

Keywords

  • Artificial neural networks
  • Escherichia coli
  • Open channel
  • Transport

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Environmental Science(all)

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