Interpreting and Estimating the Risk of Iron Failures

Abstract

Abstract Metals and particulates accumulate in the distribution system and are mobilised by hydraulic events which can result in discolouration and exceedance of regulatory standards. Traditional decision tools for targeting preventive work are single parameter, based for example on proportion of unlined iron pipe or the number of customer contacts per district metering area (DMA). We show that this approach is too simplistic and propose a multivariate Decision Tree process, using the Random Under-Sampling ensemble method. The outputs gave a classification of High or Low risk per DMA. Initial findings demonstrate an 80% success rate in identifying high risk DMAs across the supply area for a UK water company.