From reactive utilities → predictive public-health infrastructure
WaterNet AI predicts water loss and contamination before people fall sick — transforming water utilities into intelligent public-health infrastructure.
Current systems react after damage has occurred, leaving cities vulnerable to outbreaks and infrastructure failures.
They cannot explain why an anomaly happened, making it impossible to prevent future incidents.
No connection between infrastructure failures and human health impact assessment.
Provide alerts, not actionable decisions. Cities know something is wrong but not what to do.
Cities know something is wrong, but not where, why, or what to do first.
"Pressure drop detected."
"This contamination likely originated at Pipe Segment 14 due to pressure reversal after valve closure, and will affect 3 downstream wards within 6 hours if unmitigated."
From detection to explanation and prediction — that's the heart of WaterNet AI.
Not just alerts — we answer why anomalies happen and how they will spread using advanced causal AI.
Real-time virtual replica of the entire city water network for simulation and analysis.
LSTM Autoencoders & Isolation Forest detect micro-leaks, tampering, and quality deviations early.
Predict contamination probability before lab confirmation or reported illness.
Estimate exposed population, vulnerable demographics, and disease risk scores per ward.
Ranked mitigation strategies with cost, population impact, and time-to-mitigate analysis.
Live virtual replica: Pipes → edges, Valves/Tanks/Pumps → nodes
LSTM Autoencoders, Isolation Forest, ARIMA
Causal graphs, Bayesian inference, Graph NNs
Multi-source fusion, Disease correlation
Visualize real-time water flow across the city network map
Pressure anomaly + contamination event detected in the system
Machine learning models instantly identify the anomaly
Visual backtracking identifies the origin point in the network
Spread pattern and health risk forecasted for affected wards
Ranked mitigation strategies automatically recommended
| Where did it start? | Graph backtracking + Bayesian |
| Why did it happen? | Causal graph modeling |
| How will it spread? | Spatio-temporal GNNs |
| Who will be affected? | Demand + population overlays |
Reduce non-revenue water by 10-15% through early leak detection and prevention.
Prevent disease outbreaks with huge healthcare savings and protected public health.
Avoid political and legal fallout from water contamination incidents.
Enable intelligent infrastructure investment decisions based on real insights.
"WaterNet AI is a city-scale digital twin that predicts water loss and contamination before people fall sick — transforming water utilities from reactive systems into intelligent public-health infrastructure."