Data Mining for Management and Rehabilitation of Water Systems: The Evolutionary Polynomial Regression Approach

Risk-based management and rehabilitation of water distribution systems requires that company asset data are collected and also that a methodology is available to efficiently extract information from data. The process of extracting useful information from data is called knowledge discovery and at its core is data mining. This automated analysis of large or complex datasets is performed to determine significant patterns among data. There are many data mining technologies (Decision Tree, Rule Induction, Statistical analysis, Artificial Neural Networks, etc.), but not all are useful for every type of problem. This paper deals with a novel data mining methodology for pipe burst analysis, which integrates numerical and symbolic regression. This new technique is named Evolutionary Polynomial Regression and uses polynomial structures whose exponents are selected by an evolutionary search, thus providing symbolic expressions.