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一个新的区域识别框架,整合城市易洪水区的集群和延期
Qin Zheng1, Jiaxiang Lin1, Aiguo Zhang2
1Key Laboratory of Smart Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou, Fujian, 350002, China.
Journal of environmental management
|November 11, 2025
概括
本研究引入了一种新的集群算法 (CDC+) 和平面扫描约束 (DSC),以准确地绘制城市洪水区的地图. 综合框架提高了边界精度,以实现有效的灾害管理和资源分配.
科学领域:
- 环境科学 环境科学
- 地理信息科学 地理信息科学
- 计算机科学 计算机科学
背景情况:
- 准确的城市洪水易发区识别对于灾害管理至关重要,特别是在面临洪水风险升级的发展中国家.
- 现有的集群算法往往缺乏边界精度,阻碍了灾难响应和资源分配的实际应用.
- 在洪水管理中,需要精确的空间表征和合理的区域界限至关重要.
研究的目的:
- 为准确的区域边界识别提出一种新的无噪声集群算法 (CDC+).
- 引入平面扫描约束 (DSC) 算法,以提高边界划定精度,超出传统方法.
- 将CDC+和DSC整合到一个框架中,以改善城市洪水易发区的识别和灾害管理支持.
主要方法:
- 开发CDC+,一个无噪声集群算法,包含边界处理.
- 实现DSC,一个平面扫描约束算法基于Delaunay三角化,以克服凸船体边界的限制.
- 整合CDC+和DSC,以创建一个全面的区域识别框架.
主要成果:
- 拟议的CDC+算法有效地识别区域边界,同时消除噪音.
- 通过生成非凸边界,DSC算法增强了空间表示的科学性.
- 综合框架成功地确定了江省在空间上聚集的易受洪水袭击的地区,并设定了合理的边界.
结论:
- 结合的CDC+和DSC方法提供了一个强大的方法,以高的边界精度划分城市洪水易发区.
- 这一框架为科学洪水灾害管理和紧急资源的精确分配提供了重要的支持.
- 这些发现有助于在受洪水影响的地区制定更有效的城市规划和防灾战略.
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