Anticipating main breaks to reduce flood risks

A dashboard that shows water main break clusters enables our client to anticipate maintenance priorities, and better plan for capital spend.

Identifying patterns to water main breaks helps utilities efficiently maintain aging assets and mitigate flood risks to residential properties and major roads.

Our Client
Water utility


Asset value

$3 billion


500,000+ people

GHD analysed client data from over 250 Km of trunk water mains using Geographic Information Systems (GIS) software to test the idea that main breaks occur more often in clusters rather than as individual one-off events.

We collated data on all water main incidents and created a script using ArcGIS Webserving technology to perform a cluster analysis based on water main proximity. A Population at Risk (PAR) assessment was undertaken for each location and determined the flooding hazard area for each type of road.

Using an unsupervised machine learning algorithm alongside spatial data, our computers ran thousands of simulations over several weeks to understand the flooding impact on residents and main roads at different locations.

Main break likelihood at a glance

We used the data to develop an user-friendly dashboard using Arc GIS Webserving technology so that users could filter and analyse the data by proximity, break shutdown type, operational status, supply zone, minimum break numbers and size.

The dashboard also features animations showing break locations for each 5 year period of data and  key historical statistics such as pipe age at time of break, pipe material and failure event type.

Using the dashboard, our client was able to:

— Quickly grasp key issues and easily interrogate the data to highlight maintenance needs across the water main network, whether they had technical knowledge or not

— Better understand asset behaviour and make data-driven decisions

— Communicate information from the dashboard with anyone in the organisation

Find out more or get a customized solution from GHD today.