Aims & Scope

At a very basic level, Big Data means we have a lot of data. Water utilities see data from supervisory control and data acquisition (SCADA) systems, including flow statistics, online monitoring, dissolved oxygen (DO) measurements, and air flows, as well as data from laboratory information management systems (LIMS) and computerized maintenance management systems (CMMS), to name several examples. Such data is beneficial, and much of it has been around for years. Unfortunately, the way data is gathered at treatment facilities is often fragmented. There are silos of data in computer systems that don’t always talk to each other. The internet age has ushered in the ability to funnel disparate data into a single, meaningful pool of information that allows water and wastewater treatment plant operators to understand, manage, and use it to optimize plant reliability and performance. Big Data initiatives and new data management tools enable us to turn all that data into understandable, useful information that helps us become more proactive and make better decisions about plant operations.

Topics include but are not limited to;

  • Smart water management
  • Big data in water resources engineering
  • Real-time data management
  • Water distribution system
  • Water sustainability
  • Drought risk management
  • Irrigation and drainage
  • Predictive modelling of water resources
  • Remote sensing and GIS for watershed management
  • Water pollutants, characteristics and effects
  • Water supply in specific climates (e.g. arid, semi-arid)
  • Water security, including water safety plans
  • Water supply, wastewater and waterborne diseases
  • Operational intelligence of water supply system
  • Digitalization of water distribution systems
  • Artificial intelligence methods in water supply
  • Climate change impacts on water supply
  • Integrated management of water resources
  • Cascading effects between water infrastructure systems
  • Resource recovery and residuals management
  • Modelling, systems analysis, machine learning and smart water controls
  • Water treatment technologies, including reuse and recycling
  • Storage and distribution of treated non-conventional water resources.
  • Standards for non-conventional water treatment and use.
  • Water utility management including economic and social aspects
  • The use of domestic and industrial wastewater, or other non-conventional water sources.
  • Water quality and health risk assessment of non-conventional water sources
  • Planning, design, operation and management of non-conventional water treatment
  • Regulation and policy for water quality control and water resource management

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