Data-Driven Solutions for Water Management in Smart Cities

About the Study Group

The Smart Water group develops IoT-enabled monitoring and machine-learning analytics to improve urban water systems: demand forecasting, leak detection, and water-quality insights that support resilient, sustainable cities.

Objectives

• Integrate distributed IoT sensors for real-time flow/quality monitoring.
• Apply data analytics & ML for demand forecasts and leak/anomaly detection.
• Translate evidence into dashboards, alerts, and policy-ready summaries.

Main Areas of Study

• Smart Water Basics: network topology, metering, uncertainty, QA/QC.
• IoT & Real-Time Monitoring: sensor selection, low-power nodes, data pipelines.
• Analytics & ML: time-series, anomaly models, short-term forecasting.
• Practice & Policy: KPIs, reporting, and governance for sustainable operations.

Lead & Contact

Prof. Ulrike Gayh — Head of Water Technology Master Program, SRH Heidelberg
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Related

See also: Physics for a Sustainable Future (IoT & Climate Education) · AI for Sustainability — Workshop Report