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LGA’s and water utilities face mounting pressure to operate ageing networks efficiently while managing rising demand, climate variability and higher service expectations. Many organisations have built digital twins that combine hydraulic and hydrologic models with near-real-time data, but these twins are often descriptive—good at visualising conditions and diagnosing problems, yet less able to prescribe actions or prioritise interventions.
This webinar shows how advances in AI and machine learning can change that. Complementing—not replacing—physics-based models, AI/ML can automate repetitive tasks, capture and scale best practice, and create fast surrogate models for rapid scenario testing. Integrated with established modelling platforms and robust data governance, these techniques help generate, rank and explain candidate solutions—from automated calibration and anomaly detection to predictive flood mapping and pump-schedule optimisation.
Join us for a practical session on methods, integration, verification and governance, including a real-world case that demonstrates how hybrid AI+physics approaches turn descriptive twins into prescriptive decision-support platforms.

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