Real-Time Control of Urban Water Management Systems

Real-time control refers to the dynamic management of urban water networks using sensors, actuators and decision algorithms to optimise system performance. By integrating data streams – from rainfall forecasts to in-system flow measurements – and advanced control strategies such as rule-based heuristics, model predictive control and machine learning, these systems can actively regulate flows, storage and treatment processes. Applications range from reducing combined sewer overflow during storm events to optimising water supply, flood mitigation and baseflow restoration in waterways. Real-time control enhances resilience by adapting to evolving conditions, minimises infrastructure expansion, lowers operational costs and mitigates environmental impacts. Advances in communication technologies and Internet-of-Things platforms have accelerated deployment of decentralised and centralised control architectures, enabling coordinated operation of distributed assets across catchments and cities.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Real-Time Control of Urban Water Management Systems publication trend

The graph below shows the total number of articles in real-time control of urban water management systems across all publications each year (not limited to Nature Index journals).

Technical terms

Real-Time Control (RTC): A strategy that uses live data inputs and automated decision algorithms to dynamically adjust the operation of water infrastructure components, such as valves, pumps and storage, in response to changing conditions.

Model Predictive Control (MPC): An optimisation-based control method that forecasts future system behaviour over a time horizon and computes control actions by solving an optimisation problem at each step, allowing proactive adjustment.

Reinforcement Learning (RL): A machine-learning technique where an agent learns optimal control policies through interaction with the environment, using feedback on performance to improve decision-making under uncertainty.

Internet of Things (IoT): A network of interconnected sensors and actuators embedded in infrastructure assets, enabling real-time monitoring, communication and control of distributed water management components.

References

1. Coordinating Rule-Based and System-Wide Model Predictive Control Strategies to Reduce Storage Expansion of Combined Urban Drainage Systems: The Case Study of Lundtofte, Denmark. Water (2018).
2. Integrated urban water management with micro storages developed as an IoT-based solution – The smart rain barrel. Environmental Modelling & Software (2021).
3. Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation. Water (2020).
4. Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management. Sustainable Cities and Society (2022).