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AI USE CASE
AI-Powered Water Loss Detection
Detect and locate pipe leaks faster using ML analysis of real-time flow sensor data.
What it is
By applying machine learning to continuous flow and pressure data across the distribution network, utilities can detect anomalies indicative of leaks within hours rather than weeks. Early detection also prevents costly infrastructure damage and service disruptions.
Data you need
Historical and real-time flow, pressure, and consumption sensor data from IoT devices distributed across the water distribution network.
Required systems
- data warehouse
- erp
Why it works
- Adequate density of calibrated IoT flow and pressure sensors across the network before deployment.
- Strong collaboration between data engineers and network operations staff to validate anomaly thresholds.
- Continuous model retraining cadence tied to seasonal demand patterns and infrastructure changes.
- Clear escalation workflows so field crews can act on AI-generated alerts within defined SLAs.
How this goes wrong
- Sparse or low-quality sensor coverage leads to too many blind spots for reliable anomaly detection.
- High false-positive rates erode field team trust and lead to alert fatigue, causing real leaks to be ignored.
- Model drift over time as network conditions change, without scheduled retraining pipelines in place.
- Integration complexity with legacy SCADA systems delays deployment and increases costs significantly.
Example vendors
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