A smart predictive framework for system-level stormwater management optimization

Area of Science:

• Environmental Engineering
• Water Resource Management
• Control Systems

Background:

• Stormwater management faces challenges from increasing urbanization and climate change.
• Existing systems often lack adaptive and globally optimized control strategies.
• Sustainable and resilient water management requires advanced operational decision-making.

Purpose of the Study:

• To present a global real-time control (RTC) approach for sustainable and adaptive stormwater management.
• To enable smart catchment-scale optimal control of distributed detention basins.
• To evaluate the system's performance in enhancing water quality and quantity management.

Main Methods:

• A network of interconnected devices generating set-points for remote control centers.
• Global optimization algorithms calculating real-time operational decision-making targets.
• Local controllers manipulating detention basin outlets for catchment-scale control.
• Model simulations on a real-world watershed with four outlets.

Main Results:

• The autonomous RTC approach enhances stormwater management performance globally (quality and quantity).
• The system balances network flow dynamics and environmental demands, reducing erosion potential.
• Under climate change, peak flow reduction improved by up to 54% and water detention time by 14 hours.

Conclusions:

• The proposed RTC methodology offers an effective strategy for adaptive stormwater management.
• The system demonstrates resilience and improved performance under climate change scenarios.
• This approach contributes to sustainable urban water management and protection of receiving water bodies.