综合综合评估和集群算法的应用,加重城市洪水易感性的最大信息系数
Hongfa Wang1, Yu Meng1, Huiliang Wang1
1School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, Henan, 450001, PR China; Yellow River Laboratory, Zhengzhou University, Zhengzhou, Henan, 450001, PR China.
Journal of environmental management
|September 4, 2023
概括
城市化影响州的洪水敏感性. 本研究提出了一种结合聚类和综合评估的新方法,用于准确评估洪水敏感性,帮助城市规划和缓解努力.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 城市化显著改变了不同城市分区的洪水敏感性.
- 准确的洪水敏感性评估对于有效的城市规划和洪水减缓策略至关重要.
研究的目的:
- 开发一种可靠的方法,将城市洪水敏感性评估与实际洪水事件联系起来.
- 为洪水敏感度绘制提供灵活的方法,尽量减少数据要求和分类标准依赖.
主要方法:
- 使用最大信息系数 (MIC) 来确定条件因子对洪水数据的权重.
- 采用了三种不同的集群算法来产生各种各样的洪水灵敏度结果.
- 建立了一个四层分析层次流程 (AHP) 评估结构,以选择最佳的洪水易感性地图.
主要成果:
- 积极规模放大策略被确定为最有效的方法.
- 通过K-Means集群,得出了最适合的洪水敏感度地图,将州的子区域划分为四个不同的水平.
- 提出的方法表明对洪水数据的需求较低,并且独立于灵敏度级别分类标准.
结论:
- 开发的方法为州的城市规划和洪水风险管理提供了宝贵的见解.
- 建议在州的各次区域使用K-Means集群的四级洪水敏感性分类.
- 这种方法提供了一种可靠的工具,可以提高城市对洪水的抵抗力.
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