Revealing hidden problems in distribution networks using Data Science
In our series “A Watertight Approach…” we address the developments in both sensor technologies and advanced data analysis tools in order to facilitate process optimisation. Our first episode in this series focuses on the water distribution network: Revealing the hidden problems in the distribution network using AI and Data Science.
The distribution network has long been considered a black box, with treated water going in at one end, and tap water coming out at the other. However, this black box can hide any number of problems, and the fact that the infrastructure is largely underground makes it difficult to detect these. Data science can help to predict or even to prevent certain problems, ranging from water quality to quantity issues, thus creating a win-win situation for all parties involved.
While AI and machine learning approaches are already being applied in a range of asset management scenarios, our speaker, Matthew Stephenson of HAL24K, moves away from the well-known examples and presents the latest thinking about how to deal with the hidden, and sometimes uncomfortable problems that occur in the distribution network which he believes could be solved with a well-designed data science approach.
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