Data Mining Tools to Improve Buried Water Infrastructure Decision-Making

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Buried water infrastructure throughout Canada is deteriorating as the infrastructure approaches the end of its useful life. While remediation of components of the infrastructure is feasible, identifying the best remedial actions and prioritizing investment strategies between different portions of the water infrastructure, is extremely challenging. The challenges in decision-making arise since the rates of failure of water and sewer pipes are influenced by numerous external factors. Many existing approaches have relied upon nonlinear regression, an approach which is highly constrained by assumptions and constraints implicit in the methodology. This research will employ new data mining models with different types of architecture, to assess their capabilities to provide processing capabilities which are capable of performing massive parallel processing computations and are not constrained by the limiting assumptions existing in nonlinear regression.