基于深度学习的空间优化绿色和冷屋顶的实施,以减轻城市的热量
JiHyun Kim1, Suyeon Choi1, Mahdi Panahi2
1Department of Civil and Environmental Engineering, Yonsei University, Seoul, Republic of Korea.
Journal of environmental management
|April 24, 2025
概括
优化城市绿色和凉爽的屋顶可以显著减少热应激. 一个新的深度学习框架表明,广泛实施冷屋顶是缓解城市极端热的最具成本效益的方法.
科学领域:
- 城市规划是城市规划.
- 适应气候变化 适应气候变化
- 建筑科学 建筑科学
背景情况:
- 由于气候变化,城市的极端热量正在加剧.
- 有效的缓解策略对于城市弹性至关重要.
研究的目的:
- 为优化城市绿色和凉爽屋顶的实施制定一个方法框架.
- 为了最大限度地降低城市热应激的成本效益.
主要方法:
- 开发了一个基于Multi-ResNet深度学习算法的替代模型.
- 该模型是从天气研究和预测模型的数据与城市树冠模型 (WRF-UCM) 结合训练的.
- 该框架适用于大首尔地区,根据SSP585的2090-2099年气候场景.
主要成果:
- 巴雷托最佳情景涉及在89.2%的城市地区以目前的绿色屋顶成本实施冷屋顶.
- 这种情况将有效热应力指数降低8.8%,成本降低19.6%.
- 对绿色屋顶的成本效益来说,在40年内确定了117.4146.1/m的最佳成本范围.
结论:
- 深度学习技术可以为城市规划提供高效的定量评估.
- 与传统模型相比,拟议的框架大大降低了计算需求.
- 这种方法通过优化绿色和凉爽屋顶策略来支持气候适应性的城市建筑规划.
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