A smart predictive framework for system-level stormwater management optimization.

This study presents a global real-time control (RTC) approach for sustainable and adaptive management of stormwater. A network of inter-connected devices are assumed to dynamically generate the required set-points for the system actuators at the remote control center where global optimization algorithms calculate real-time operational decision-making target values. These target values activate the local controllers to manipulate the spatially distributed detention basin's outlets enabling a smart catchment scale optimal control. A real world watershed with four outlets to a nearby watercourse is chosen to test the applicability and efficiency of the proposed dynamic control approach, based on model simulation results. Results show that the proposed autonomous control approach has the ability to enhance the global performance of the stormwater management system in terms of quality and quantity to balance the network flow dynamics and environmental demands, while reducing the potential for erosion of receiving water bodies. Climate change is specifically discussed as a challenge for the designed control framework. Although, the performance criteria are shown to be affected by the increased rainfall intensities compared to actual rainfall scenarios, the proposed methodology still improves the peak flow reduction and detention time of water, at global scale, up to 54% and 14 h respectively under climate change conditions.