AI USE CASE
ML-Driven Stormwater Runoff Prediction
Predict stormwater volumes and optimize retention systems using ML on rainfall and terrain data.
What it is
By combining rainfall forecasts, terrain topology, and drainage capacity data, machine learning models can predict stormwater runoff volumes hours or days in advance. Over time, the system learns seasonal and climate patterns to continuously improve prediction accuracy.
Data you need
Historical rainfall measurements, high-resolution terrain/elevation data, drainage network capacity data, and real-time sensor feeds from the stormwater infrastructure.
Required systems
- data warehouse
Why it works
- Deploy a dense, well-maintained sensor network capturing real-time rainfall, flow, and basin levels across the catchment area.
- Integrate predictions directly into SCADA or operational control systems to enable automated or semi-automated responses.
- Engage field operators early in the design phase to ensure model outputs are actionable and trusted by the team.
- Establish a regular model retraining pipeline that incorporates new seasonal data and post-event analysis.
How this goes wrong
- Insufficient historical sensor data leads to poorly calibrated models that underperform during extreme weather events.
- Low data quality from aging or poorly maintained IoT sensors introduces noise that degrades prediction accuracy.
- Lack of integration between the ML system and operational controls means predictions are not acted upon in time.
- Models trained on historical patterns fail to generalize to new climate conditions or urban development changes.
When NOT to do this
Do not deploy this system if the drainage infrastructure lacks real-time sensor coverage, as predictions without live data feedback become unreliable during the critical storm onset window.
Example vendors
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