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