A rule-based multi-objective optimization framework for flood control in coastal urban river networks under extreme...

Area of Science:

• Environmental Engineering
• Water Resource Management
• Urban Hydrology

Background:

• Coastal urban river networks face escalating flood risks due to extreme rainfall and tidal influences.
• Effective management of hydraulic infrastructure is crucial for mitigating these compound flood challenges.

Purpose of the Study:

• To develop a rule-based multi-objective optimization framework for deriving operating rules for hydraulic infrastructure.
• To minimize overflow risk and operational costs in coastal urban river networks.

Main Methods:

• Coupling the Storm Water Management Model (SWMM) with the NSGA-II algorithm to create a multi-objective optimization model.
• Applying the framework to a river network in Fuzhou City under various rainfall-tide scenarios.
• Evaluating performance using metrics for convergence, adaptability, and identifying optimal water-level rule-based control (WL-RBC) schemes.

Main Results:

• The optimization framework significantly reduced exceedance depth (48.99%-77.22%) and energy consumption (>92%) compared to the original scheme.
• Water-level rule-based control (WL-RBC) schemes demonstrated adaptability under diverse hydrological conditions (DCR 0.74-1.0).
• Enhanced hydraulic performance included better storage utilization, reduced water-level fluctuations, shorter inundation durations, and a shift to gate-prioritized operations, yielding substantial energy savings.

Conclusions:

• The proposed framework effectively enhances the resilience, adaptability, and energy efficiency of coastal urban river networks facing compound flooding.
• Optimized operating rules for hydraulic infrastructure are vital for sustainable urban water management in flood-prone areas.
• The study provides a scalable solution for improving flood control and operational efficiency in similar urban environments worldwide.