Abstract
Abstract Water utilities collect vast amounts of data, but they are stored and utilised in silos. Machine learning (ML) techniques offer the potential to gain deeper insight from such data. We set out a Big Data framework that for the first time enables a structured approach to systematically progress through data storage, integration, analysis, and visualisation, with applications shown for drinking water quality. A novel process for the selection of the appropriate ML method, driven by the insight required and the available data, is presented. Case studies for a water utility supplying 5.5 million people validate the framework and provide examples of its use to derive actionable information from data to help ensure the delivery of safe drinking water.
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