Multi-objective optimization for green-grey infrastructures in response to external uncertainties

Abstract

The optimized green-grey infrastructures are promising solutions to combat the urban flood and water quality problems which have been severe owe to the increasing urbanization and climate change. However, the focusses in existing researches have been either on finding the best strategy by scenario analysis method or optimal design of LID practices under the hypothesis of unchanged grey infrastructures. Little is known regarding the synergistic effect of synchronous optimization design of both green and grey infrastructures. In the study, we conduct green-grey infrastructures synchronous optimization by modifying the decision variables of traditional simulation-optimization frameworks and investigate how external uncertainties will affect their performance. The methodology was applied to a case study in Suzhou, China. The results showed that although the cost of green-grey synchronous optimized scenarios is lower than that of green optimized only scenarios by 1.69–4.19 thousand USD per km2, the runoff/pollutants reductions of green-grey synchronous optimized scenarios are 0.11%–5.24% higher than that of green optimized only scenarios. In the green-grey synchronous optimized scenarios, green infrastructures can contribute to runoff/pollutants control by 50%–63%/62%–70%, while grey infrastructures can contribute to the remaining part by 37%–50%/30%–38%.