A Data Mining Approach to Modelling of Water Supply Assets

The economic and social costs associated with pipe bursts and associated leakage problems in modern water supply systems are rapidly rising to unacceptable high levels. Pipe burst risks depend on a number of factors which are extremely difficult to characterise. A part of the problem is that water supply assets are mainly situated underground, and therefore not visible and under the influence of various highly unpredictable forces. This paper proposes the use of advanced data mining methods in order to determine the risks of pipe bursts. For example, analysis of the database of already occurred burst events can be used to establish a risk model as a function of associated characteristics of bursting pipe. (its age, diameter, material of which it is built, etc.), soil type in which the pipe is laid, climatological factors (such as temperature), traffic loading, etc. In addition to immediate aid with the choice of pipes to be replaced, the outlined approach opens completely new avenues in asset modelling. The condition of an asset such as a water supply network deteriorates with age. With reliable risk models, addressing the evolution of risk with aging asset, it is now possible to plan optimal rehabilitation strategies in advance, before the burst actually occurs.