UTS Data Science Institute are leaders in smart water resource and infrastructure management.
The rapid emergence of big data in the infrastructure sector has unlocked a range of exciting new opportunities for transforming the way that risk assessment, maintenance scheduling and fault analysis is conducted.
Our capabilities in water infrastructure include:
- forecasting water pipe failure and risk
- automated water leakage identification and inferencing
- multi-faceted big data fusion and visualisation
- optimal chemical dosing of wastewater
- prediction of sewer corrosion
- water demand forecasting.
Our researchers have worked with more than 30 water utilities around the world, examining data from over 10 million pipe assets and one million failure records. Working with clients, the team fuses asset attributes, environmental conditions and machine learning algorithms to build predictive platforms to better target asset maintenance and water quality treatment to shift industry to a more preventative approach.
The emergent risk of water pipe failure depends on a complex interaction between pipe material, network aging, soil movement, corrosion, water pressure, usage trends, historical maintenance action, and much more.
With machine learning, we can simultaneously learn from data that describes the behaviour and performance of literally millions of pipes and that documents thousands of failures. Leveraging this approach, we have developed water pipe failure forecasting and analysis tools that can predict at-risk network elements, forecast potential failures and reveal the key drivers of historical failure events.
The result is a new data-driven paradigm for managing water networks that better prioritises and paces maintenance programs, lowers maintenance costs and improves network reliability, while simultaneously reducing human effort.