根据全球城市的可解释深度学习模型,对城市热岛表面进行分级和调查因子权重
Kangning Li1, Yunhao Chen2, Jinbao Jiang1
1College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
Environment international
|September 14, 2023
概括
这项研究引入了一个新的全球表面城市热岛 (SUHI) 评级系统,识别了从凉爽到高价值热岛的五个不同的等级. 它揭示了基于气候的变化,并量化了影响SUHI强度和频率的关键因素.
科学领域:
- 城市气候学 城市气候学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 城市化加剧了表面城市热岛 (SUHI) 效应,对城市生态系统和人类舒适性产生负面影响.
- 有效的SUHI缓解需要精确监测时空变化,并了解其控制因素.
- 全球SUHI评级和定量因素分析对于制定全球缓解策略至关重要.
研究的目的:
- 为全球城市提出一套新的SUHI评分系统.
- 量化评估影响全球不同SUHI指标和等级的因素权重.
- 为有效的缓解计划提供有关SUHI控制的见解.
主要方法:
- 在SUHI分级中使用了聚合层次的分类.
- 使用一个可互操作的机器学习模型TabNet来量化因子权重.
- 分析包括时空变化,基于气候的差异,以及对各种SUHI等级的因素贡献.
主要成果:
- 全球城市被分为五个SUHI等级:表面城市冷岛 (SUCI),微不足道,低价值,中价值和高价值.
- 气候显著影响SUHI等级,干旱地区白天显示SUCI,晚上显示高值SUHI.
- 植被差异是白天SUHI (27%) 的一个主要因素,而白色和夜间光线是夜间频率的关键 (45%).
结论:
- 开发的SUHI分级系统为了解全球SUHI变异提供了一个框架.
- 因素贡献在SUHI等级之间有所不同,人为热流显著影响更高价值的夜间SUHI.
- 这项研究提供了关于SUHI等级和控制的关键数据,以告知未来的全球缓解努力.
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