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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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Water Research X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589914725000866 |
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| author | Qinghe Hou Hanwen Xu Mingkun Xie Pingjia Luo Yuning Cheng |
| author_facet | Qinghe Hou Hanwen Xu Mingkun Xie Pingjia Luo Yuning Cheng |
| author_sort | Qinghe Hou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5e8cf49badb64dbda15d2faa564dec27 |
| institution | Kabale University |
| issn | 2589-9147 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Water Research X |
| spelling | doaj-art-5e8cf49badb64dbda15d2faa564dec272025-08-20T03:58:11ZengElsevierWater Research X2589-91472025-09-012810038710.1016/j.wroa.2025.100387A cellular automata coupled multi-objective optimization framework for blue-green infrastructure spatial allocationQinghe Hou0Hanwen Xu1Mingkun Xie2Pingjia Luo3Yuning Cheng4School of Architecture and Design, China University of Mining and Technology, Xuzhou 221116, PR China; Corresponding authors.Department of Geosciences and Natural Resource Management, University of Copenhagen, Frederiksberg 1958, DenmarkCollege of Civil and Transportation Engineering, Hohai University, Nanjing 210024, PR ChinaSchool of Architecture and Design, China University of Mining and Technology, Xuzhou 221116, PR China; Corresponding authors.College of Architecture, Southeast University, Nanjing 210096, PR China; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S2589914725000866Blue-green infrastructureCA-based hydrological modelLandscape-hydrology-cost effectivenessMulti-objective optimizationUrban stormwater management |
| spellingShingle | Qinghe Hou Hanwen Xu Mingkun Xie Pingjia Luo Yuning Cheng A cellular automata coupled multi-objective optimization framework for blue-green infrastructure spatial allocation Water Research X Blue-green infrastructure CA-based hydrological model Landscape-hydrology-cost effectiveness Multi-objective optimization Urban stormwater management |
| title | A cellular automata coupled multi-objective optimization framework for blue-green infrastructure spatial allocation |
| title_full | A cellular automata coupled multi-objective optimization framework for blue-green infrastructure spatial allocation |
| title_fullStr | A cellular automata coupled multi-objective optimization framework for blue-green infrastructure spatial allocation |
| title_full_unstemmed | A cellular automata coupled multi-objective optimization framework for blue-green infrastructure spatial allocation |
| title_short | A cellular automata coupled multi-objective optimization framework for blue-green infrastructure spatial allocation |
| title_sort | cellular automata coupled multi objective optimization framework for blue green infrastructure spatial allocation |
| topic | Blue-green infrastructure CA-based hydrological model Landscape-hydrology-cost effectiveness Multi-objective optimization Urban stormwater management |
| url | http://www.sciencedirect.com/science/article/pii/S2589914725000866 |
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