Macroeconomics

Big data

Informatics

Engineering

Ecology

Analytics

Biology

Smart city

Computer security

Smart grid

Water resource management

Economics

Data mining

Civil engineering

Computer science

Water consumption

Construction engineering

Environmental planning

Mechanical engineering

Environmental engineering

Demand management

Internet of Things

Water cycle

Environmental resource management

Sociology

Environmental science

Data science

Metering mode

Consumption (sociology)

Smart meter

Electrical engineering

Social science

Environmental Modelling & Software

Current practice for the design of an urban water system usually relies on various models that are often founded on a number of assumptions on how bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters and advanced data analytics allow for a new era of using the continuous 'big data' generated by these meter fleets to create an intelligent system for urban water management to overcome this problem. The aim of this research is to provide infrastructure planners with a detailed understanding of how granular data generated by an intelligent water management system (Autoflow©) can be utilised to obtain significant efficiencies throughout different stages of an urban water cycle, from supply, distribution, customer engagement, and even wastewater treatment.

Re-engineering traditional urban water management practices with smart metering and informatics