一个基于测量的框架,集成机器学习和外界温度调节的形态动态
Niloufar Alinasab1, Negar Mohammadzadeh2, Alireza Karimi3
1Department of Atmospheric and Geospatial Data Sciences, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary. nfar.alinasab@gmail.com.
International journal of biometeorology
|April 21, 2025
概括
机器学习模型使用城市峡谷特征准确预测室外热舒适度 (OTC). 街道的方向和宽度对UTI,PET和PMV等热指数产生重大影响,有助于城市减热策略.
科学领域:
- 城市气候学和环境科学.
- 人工智能在环境研究中的应用.
背景情况:
- 户外热舒适 (OTC) 对城市居民的福祉至关重要.
- 城市形态学显著影响当地微气候和热感知.
- 现有的模型往往缺乏在不同的城市环境中进行全面验证.
研究的目的:
- 研究机器学习 (ML) 模型,城市形态特征和户外热舒适度指标之间的关系.
- 评估和优化ML模型,用于预测通用热气候指数 (UTCI),生理等效温度 (PET) 和预测平均投票率 (PMV).
- 通过使用SHAP值来确定影响OTC的最有影响力的城市形态因素.
主要方法:
- 夏季在173个城市峡谷进行了全面的现场测量.
- 使用贝叶斯优化评估和优化六个ML模型 (例如,CatBoost,随机森林,XGBoost).
- 使用SHAP值来分析特征重要性,以了解城市形态学对OTC的影响.
主要成果:
- CatBoost,随机森林和XGBoost在UTCI,PET和PMV预测方面表现出分别优异的性能.
- 城市峡谷的方向 (90°和180°) 和街道宽度被确定为影响OTC的关键因素.
- 路宽成为PMV预测的最关键因素,突出了复杂的热力学.
结论:
- 机器学习模型提供了基于城市形态学的OTC预测的强有力的方法.
- 城市设计元素,特别是街道的方向和宽度,在调节热环境方面发挥着至关重要的作用.
- 这些发现有助于通过明智的设计策略来理解和减轻城市热应激.
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