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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-07

AUTHORS

Yoon-Seok Timothy Hong, Byeong-Cheon Paik

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 minimize microbial pollution risk to the public. This paper introduces a hybrid modeling 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. More... »

PAGES

695-707

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00477-011-0538-9

DOI

http://dx.doi.org/10.1007/s00477-011-0538-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028672533


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