基于遗传算法的承洪绿色基础设施规划优化：以郑州为例

况达; 张印豪; 杨筱彤; 李相逸

doi:10.13300/j.cnki.hnlkxb.2026.01.012

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基于遗传算法的承洪绿色基础设施规划优化：以郑州为例

城乡融绿高质量发展专题 | 更新时间：2026-11-05

- 基于遗传算法的承洪绿色基础设施规划优化：以郑州为例
- Genetic-algorithm-based optimization of flood-resilient green infrastructure planning： a case study of Zhengzhou
- 华中农业大学学报 2026年45卷第1期页码：130-144
- 作者机构：
  
  1.深圳大学建筑与城市规划学院，深圳 518060
  2.亚热带建筑与城市科学重点实验室，深圳 518060
- 作者简介：
  
  况达， E-mail：da.kuang@szu.edu.cn
  李相逸， E-mail：lixiangyi@szu.edu.cn
- 基金信息：
  
  广东省自然科学基金项目(2024A1515011422;2025A1515010973);深圳市自然科学基金面上项目(JCYJ20250604182411016)
- DOI：10.13300/j.cnki.hnlkxb.2026.01.012
  中图分类号： TU985.12
- 收稿：2025-11-14，
  
  纸质出版：2026-01-15
- 稿件说明：
移动端阅览
况达，张印豪，杨筱彤，等.基于遗传算法的承洪绿色基础设施规划优化：以郑州为例［J］.华中农业大学学报，2026，45（1）：130‐144.

KUANG Da,ZHANG Yinhao,YANG Xiaotong,et al.Genetic-algorithm-based optimization of flood-resilient green infrastructure planning： a case study of Zhengzhou[J].Journal of Huazhong Agricultural University,2026,45(01):130-144.
况达，张印豪，杨筱彤，等.基于遗传算法的承洪绿色基础设施规划优化：以郑州为例［J］.华中农业大学学报，2026，45（1）：130‐144. DOI： 10.13300/j.cnki.hnlkxb.2026.01.012.

KUANG Da,ZHANG Yinhao,YANG Xiaotong,et al.Genetic-algorithm-based optimization of flood-resilient green infrastructure planning： a case study of Zhengzhou[J].Journal of Huazhong Agricultural University,2026,45(01):130-144. DOI： 10.13300/j.cnki.hnlkxb.2026.01.012.

摘要

传统的灰色基础设施由于忽视自然水文过程与生态系统服务的协同作用，在应对复合型灾害链方面表现出明显局限。绿色基础设施（green infrastructure，GI）通过基于自然过程的调控与多功能协同，被认为是弥补上述局限的有效途径。在此背景下，为实现GI的精确选址与类型配置优化，以郑州市为例，构建了融合多准则决策评价与遗传算法的规划优化框架。基于自然地理与气候、基础设施敏感性、自然环境响应和经济社会影响4个维度构建指标体系，在GIS平台上形成GI空间部署优先级模型，并引入遗传算法，在多目标、多约束条件下对不同优先区内承洪GI的类型与规模进行定量优化配置。结果表明，该方法能够在兼顾承洪安全与建设约束的前提下，识别承洪GI的高优先级部署区与关键控制片区，优化不同类型GI的组合配置，实现从空间优先级识别到类型配置优化的闭环规划过程。研究表明，多准则空间评价与遗传算法的耦合可为特大城市洪涝韧性提升提供可量化的决策依据与可复制的计算框架，为快速城市化地区承洪导向的GI布局提供技术支撑。

Abstract

Traditional grey infrastructure has shown significant limitations in responding to complex disaster chains due to neglecting the synergistic effects of natural hydrological processes and ecosystem services.Green infrastructure （GI），by leveraging natural processes for regulation and achieving multi-functional synergy，was regarded as an effective way to overcome these limitations.This article took Zhengzhou City as an empirical case to develop a framework for the optimization of planning GI for flood-resilience that integrates multi-criteria decision-making and evaluation with genetic algorithm.An indexes system was constructed from four dimensions including the natural geography and climatic baseline，infrastructure sensitivity，natural environmental response，and the socio-economic impacts.A spatial deployment priority model of GI was built on a GIS platform.On this basis，a genetic algorithm was introduced to perform quantitative optimization of the types and scales of GI for flood-resilience within each priority zone under multiple objectives and constraints to obtain near-optimal GI layout schemes for different regions.The results showed that the proposed framework can effectively identify the high-priority deployment areas and key control zones of GI for flood-resilience in Zhengzhou City while simultaneously considering the safety of flood-resilience and construction constraints，optimize the portfolio configuration of different types of GI，and achieve a closed-loop planning process from spatial priority identification to type allocation optimization.It is confirmed that coupling multi-criteria spatial evaluation with a multi-objective genetic algorithm can provide quantifiable basis for planning and decision-making，and replicable computational framework for improving flood-resilience in high-density megacities.It will provide a transferable technical reference for constructing the layout of flood-resilience-oriented GI in other rapidly urbanizing areas of the same type.

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