在热浪期间微型建模城市热岛危险:都灵的案例研究
Tanguy Houget1,2, Valeria Garbero3, Marco Piras2
1Laboratoire de Mécanique des Fluides et d'Acoustique (LMFA), UMR5509, Université de Lyon, Ecole Centrale de Lyon, CNRS, Université Claude Bernard Lyon 1, INSA Lyon, 36 Avenue Guy de Collonge, 69130 Ecully, France.
概括
机器学习模型使用土地使用和地形数据准确预测城市热岛 (UHI) 效应. 卷积神经网络 (CNN) 显示出强度,即使没有数值天气预测 (NWP) 数据,也表现良好.
科学领域:
- 城市气候学 城市气候学
- 机器学习在环境科学中的应用
- 地理空间数据分析.
背景情况:
- 在极端高温期间,对评估城市热岛 (UHI) 风险至关重要.
- 预测UHI效应需要具有高空间分辨率和可扩展性的模型,这目前具有挑战性.
- 现有的方法在为大规模城市温度预测而努力平衡准确性和计算效率.
研究的目的:
- 开发和评估机器学习 (ML) 算法,用于预测聚合规模的UHI效应.
- 为了比较多重线性回归 (MLR) 和卷积神经网络 (CNN) 模型的性能.
- 评估将数值天气预报 (NWP) 数据纳入机器学习模型的影响.
主要方法:
- 开发了使用六个高分辨率城市参数的ML模型:地形,几何和土地使用.
- 训练和评估MLR和CNN模型,使用来自都灵的NetAtmo公民气象站 (CWS) 的温度数据.
- 通过从NWP模型中添加第七个预测因素来评估性能改善.
主要成果:
- 美国CNN的平均平方根误差 (RMSE) 低于1.19°C,略高于MLR的平均平方根误差 (RMSE高达1.22°C).
- 即使没有NWP数据,CNN的表现仍然很强,与包括NWP数据的MLR模型相比.
- 分析突出了城市温度变化的主要空间驱动因素,证实了ML在城市气候建模中的潜力.
结论:
- 机器学习,特别是CNN,为预测城市热岛效应提供了可扩展和准确的方法.
- CNNs表现出强性,在有限的输入数据中表现良好,减少对复杂的NWP模型的依赖.
- 对空间驱动因素和数据驱动模型的进一步研究对于改善城市气候预测和减轻热风险至关重要.
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