Inference model derivation with a pattern analysis for predicting the risk of microbial pollution in a sewer system

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@Article{Hong:2012:SERRA,
  author =       "Yoon-Seok Timothy Hong and Byeong-Cheon Paik",
  title =        "Inference model derivation with a pattern analysis for
                 predicting the risk of microbial pollution in a sewer
                 system",
  journal =      "Stochastic Environmental Research and Risk
                 Assessment",
  year =         "2012",
  volume =       "26",
  number =       "5",
  pages =        "695--707",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Fecal
                 coliform bacteria, Water quality modelling,
                 Multivariate inference model derivation, Neural
                 network-based pattern analysis, Self-Organising Feature
                 Maps, Evolutionary process model induction system,
                 Grammar-based genetic programming",
  ISSN =         "1436-3240",
  DOI =          "doi:10.1007/s00477-011-0538-9",
  size =         "13 pages",
  abstract =     "Developing a mathematical model for predicting fecal
                 coliform bacteria concentration is very important
                 because it can provide a basis for water quality
                 management decisions that can minimise microbial
                 pollution risk to the public. This paper introduces a
                 hybrid modelling methodology which is a combined use of
                 a neural network-based pattern analysis and an
                 evolutionary process model induction system. The neural
                 network-based pattern analysis technique is applied to
                 extract knowledge on inter-relationships between fecal
                 coliform concentrations and other measurable variables
                 in a sewer system. Based on the result of neural
                 network-based pattern analysis, an evolutionary process
                 model induction system is used to derive mathematical
                 inference models that can predict fecal coliform
                 bacteria concentration from easily measurable variables
                 instead of directly measuring fecal coliform bacteria
                 concentration in a sewer system. The neural
                 network-based pattern analysis extracts that
                 temperature and ammonia concentration are the most
                 important driving forces leading to an increase in
                 fecal coliform bacteria concentration in the sewer
                 system at Paraparaumu City, New Zealand. Fecal coliform
                 bacteria concentration is also positively correlated
                 with dissolved phosphorus and inversely with flow rate.
                 The multivariate inference models that are able to
                 predict fecal coliform bacteria concentration are
                 successfully derived as functions of flow rate,
                 temperature, ammonia, and dissolved phosphorus in the
                 form of understandable mathematical formulae using the
                 evolutionary process model induction system, even if a
                 priori mathematical knowledge of the dynamic nature of
                 fecal coliform bacteria is poor. The multivariate
                 inference models evolved by the evolutionary process
                 model induction system produce a slightly better
                 performance than the multi-layer perceptron neural
                 network model.",
  affiliation =  "Department of Urban Engineering, London South Bank
                 University, 103 Borough Road, London, SE1 0AA UK",
}

Genetic Programming entries for Yoon-Seok Hong Byeong-Cheon Paik

Citations