Multi-objective phased optimization framework of gray-green-blue infrastructure for synergistic runoff control in...

Area of Science:

• Environmental Engineering
• Urban Planning
• Hydrology

Background:

• Gray-Green-Blue Infrastructure (GGBI) is crucial for urban flood mitigation, integrating drainage and source control.
• Conventional optimization models often neglect pumping stations due to system complexity and data demands.
• Existing hydrological models require extensive, high-quality data, limiting application in data-scarce regions.

Purpose of the Study:

• To propose a multi-objective phased optimization framework for GGBI flood control.
• To incorporate pumping station drainage flow as a dynamic variable linked to Green Infrastructure (GI) scale.
• To optimize for Annual Total Cost (ATC) and Runoff Volume Reduction Rate (RVRR) in data-limited scenarios.

Main Methods:

• Developed a two-phase optimization framework using water surface ratio and GI as decision variables.
• Phase I: Preliminary planning in data-scarce regions, determining variable ranges.
• Phase II: Spatial layout optimization using Phase I outputs as constraints.

Main Results:

• Pumping stations facilitate rapid river discharge, controlling flood levels within limited Green-Blue Infrastructure (GBI) space.
• Phase I optimization yielded specific ranges for pumping station flow, water surface ratios, and GI implementation.
• The model establishes scientific constraints for Phase II spatial optimization, enabling effective GGBI planning.

Conclusions:

• The phased optimization model offers an innovative solution for flood control in data-scarce regions.
• It transforms pumping station capacity into a dynamic variable, providing quantitative decision-making for GGBI.
• This approach supports GGBI planning under constraints of limited budgets and space.