Artificial intelligence, digital twins and dynamic resilience
Bruce Johnson (Jacobs)
Borja Valverde-Pérez (Technical University of Denmark (DTU))
Christoffer Wärff (Lund University)
Douglas Lumley (DHI Sweden AB)
Elena Torfs (Universiteit Gent)
Ingmar Nopens (Universiteit Gent)
Lloyd Townley (Nanjing Smart Technology Development Co. Ltd, Nanjing University)
Zoran Kapelan (TU Delft - Sanitary Engineering)
Emma Weisbord (Royal HaskoningDHV Digital)
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Abstract
Global digitalisation and the accelerated development of digital technologies and analytical tools have ushered rapid changes in how humans interact with the different sectors of our society. This has had a significant impact on the international water industry, as described in this book. The amount of data we have at our fingertips is typically much more than any person or group can effectively use, so help is needed to improve our decisionmaking based on these large data sets. These vast datasets are generated intensively from instrumentation mounted in water/ wastewater assets (see Chapter 2) leading to vast data silos of individually formatted and difficult to access data. For example, data logged by water companies is typically done at 15 min intervals, equating to 87,000 data points per annum. When multiple parameters are measured and logged for water and wastewater systems, it can result in millions of data points per annum. Processing this data can present a significant challenge for standard spreadsheet-based packages; therefore, new methods are required to convert this information into valuable insights.
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