A cellular automata coupled multi-objective optimization framework for blue-green infrastructure spatial allocation
Blue-green infrastructure (BGI) has emerged as a critical nature-based strategy for enhancing urban stormwater management and ecological resilience. However, optimizing BGI allocation at a city scale remains challenging due to the complex spatial heterogeneity of BGI elements and the need to balance...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Water Research X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589914725000866 |
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| Summary: | Blue-green infrastructure (BGI) has emerged as a critical nature-based strategy for enhancing urban stormwater management and ecological resilience. However, optimizing BGI allocation at a city scale remains challenging due to the complex spatial heterogeneity of BGI elements and the need to balance hydrological, ecological, and economic trade-offs. In this study, a multi-objective optimization framework was developed to address these challenges by integrating a Cellular Automata (CA)-based hydrological model with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The CA-based model enables grid-resolution simulation of surface runoff processes, and the optimization algorithm identifies spatial configurations of BGI to address landscape-hydrology-cost effectiveness trade-offs. The framework was applied to the Heping River catchment in Nanjing, China, under a 20-year return period storm scenario. The optimized BGI allocation solutions achieved an average increase of 7.45 % in water bodies and 19.92 % in green stormwater infrastructures, alongside a 20.15 % reduction in impervious surfaces. These improvements corresponded to a 19.73 % increase in landscape objective, a 27.55 % improvement in hydrology performance, and a 26.59 % reduction in life-cycle cost (LCC). In contrast to existing methods that primarily rely on semi-distributed models such as SWMM, the proposed framework advances spatial precision by explicitly modelling hydrological processes at the grid level and allowing for fine-grained spatial allocation of BGI elements. This integration provides a scalable and transferable decision-support tool for urban planners and decision makers seeking to maximize the multifunctional benefits of BGI allocation. |
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| ISSN: | 2589-9147 |