Dammika Vitanage, Corinna Doolan, Lucinda Maunsell, Bronwyn Cameron, Fang Chen, Yang Wang, Zhidong Li
A Sydney Water and Data61 collaboration is researching advanced analytics approaches to solve water industry challenges, including water pipe failure prediction, customer segmentation demand analysis, sewer corrosion prediction, optimising water quality, predicting sewer chokes, and prioritising active leakage detection areas, to achieve better outcomes for customers and to deliver world class network performance. Both organisations have partnered to understand complex data sets that can be translated into knowledge. These numbers add insight. They help to see further, understand deeper and see it sooner. Within this partnership we have developed skills on how to think about it, how to use it, and how to value it. This paper outlines how Sydney Water has progressed on predictive analytics to develop capabilities using machine learning to develop tools of value to operations, shareholders and customers. The collaborative effort on data analytics in these projects has used machine learning to predict a number of core requirements on pipes or processes for Sydney Water. The focus of the research is to learn from the current operation data and identify previously unknown or unconfirmed relationships. The aim of doing this is to improve the prediction of the required needs. In these projects, integrating current knowledge and expertise with data analytics has demonstrated promising values in predicting asset performance.