Multi-objective decision-making for green infrastructure planning: Impacts of rainfall characteristics and infrastructure configuration

Abstract

Rapid urbanization has caused significant water-related problems in urban areas, including flooding and pollution. Green infrastructure has emerged as an effective solution within the realm of nature-based approaches to address these problems. However, there is a need for multi-objective decision-making in green infrastructure planning to strike a balance among various benefits, including those related to the anthroposphere and hydrosphere. Unfortunately, the impacts of rainfall characteristics and infrastructure configuration on multi-objective optimization outcomes are not well understood. To bridge this knowledge gap, a multi-objective optimization tool was developed in this study considering area, location, and hydrological linkages across green infrastructures. This tool combines the Storm Water Management Model (SWMM) and the Strength Pareto Evolutionary Algorithm 2. The proposed tool was implemented in a district-scale research region characterized by seven different rainfall patterns and four return periods. The results show that the proposed tool, integrated with the calibrated and validated SWMM, can provide rational configuration solutions for the target region. Rainfall characteristics significantly affect the cost-efficiency curve and layout of the green infrastructure, given their diverse nature. Solutions optimized by this approach yield optimal economic and environmental benefits. Notably, solutions incorporating hydrological linkages exhibit reduction rates approximately twice as high as those without such linkages. These findings may provide a workable theoretical basis for nature-based solutions and valuable guidance for urban water management.