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. Typical deployments reduce non-revenue water (NRW) losses by 15–30%, translating to hundreds of thousands of euros in recovered water annually for mid-size utilities. Leak localization capabilities reduce field investigation time by 40–60%, allowing maintenance crews to prioritize repairs efficiently. 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.
When NOT to do this
Don't implement this if your network has fewer than 30% of distribution zones equipped with digital meters or flow sensors — the data foundation simply isn't there yet.
Vendors to consider
Sources
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