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首页> 外文期刊>Science of the total environment >Statistical prediction of the nocturnal urban heat island intensity based on urban morphology and geographical factors - An investigation based on numerical model results for a large ensemble of French cities
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Statistical prediction of the nocturnal urban heat island intensity based on urban morphology and geographical factors - An investigation based on numerical model results for a large ensemble of French cities

机译：基于城市形态和地理因素的夜间城市热岛强度的统计预测 - 基于法国城市大集合的数值模型的调查

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摘要

Taking into account meteorological data in urban planning increases in relevance in the context of changing climate and enhanced urbanisation.The present article focusses on the nocturnal urban heat island intensity (UH1I) simulated with a physically based atmospheric model for>200,000 Reference Spatial Units (RSU), which correspond to building patches delimited by roads or water bodies in 42 French urban agglomerations. First are investigated the statistical relationships between the UHI1 and six predictors: Local Climate Zone, distance to the agglomeration centre, population, distance to the coast, climatic region, and elevation differences. It is found that the maximum UHII of an agglomeration increases proportional to the logarithm of its population, decreases for cities closer than 10 km to the coast, and is shaped by the regional climate. Secondly, a Random Forest model and a regression-based model are developed to predict the UHII based on the predictors. The advantage of the regression-based model is that it is easier to understand than the black box Random Forest model. The Random Forest model is able to predict the UHII with <0.5 K absolute error for 54% of the RSU. The regression-based model performs slightly worse than the Random Forest model and predicts the UHII with <0.5 K absolute error for 52% of the RSU. A future challenge is to conduct a similar investigation at global scale, which is to date limited by the availability of a robust description of urban form and functioning.
机译：考虑到城市规划中的气象数据在改变气候和增强的城市化方面的相关性增加。目前文章侧重于夜行城市热岛强度（UH1I），用实际基于物理基础的大气模型，适用于> 200,000个参考空间单位（RSU ），它对应于42个法国城市集群中的道路或水体界定的建筑贴片。首先研究了UHI1和六个预测因子之间的统计关系：局部气候区，与凝聚中心的距离，人口，到海岸，气候区域和高度差异。结果发现，聚集的最大UHII与其人口对数成正比，对于距离海岸超过10公里的城市减少，并被区域气候塑造。其次，开发了一种随机林模型和基于回归的模型来基于预测器来预测UHII。基于回归的模型的优点是比黑匣子随机森林模型更容易理解。随机森林模型能够预测UHII，为54％的RSU具有<0.5 k绝对误差。基于回归的模型比随机林模型略差差，并预测UHII为<0.5 k绝对误差为52％的RSU。未来的挑战是在全球范围内进行类似的调查，这是迄今为止有限的城市形态和运作的可用性的可用性。

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  • 来源
    《Science of the total environment》 |2020年第1期|139253.1-139253.18|共18页
  • 作者

    Thomas Gardes; Robert Schoetter; Julia Hidalgo; Nathalie Long; Eva Marques; Valery Masson;

  • 作者单位

    CNRM UMR 3589 Universite Federate de Toulouse Meteo-France/CNRS 42 avenue Gaspard Coriolis 31057 Toulouse France;

    CNRM UMR 3589 Universite Federate de Toulouse Meteo-France/CNRS 42 avenue Gaspard Coriolis 31057 Toulouse France;

    LISST Universite Federate de Toulouse - CNRS 5 allies Antonio Machado 31058 Toulouse France;

    UMR UENSs La Rochelle Universite - CNRS 2 rue Olympe de Gouges 17000 La Rochelle France;

    CNRM UMR 3589 Universite Federate de Toulouse Meteo-France/CNRS 42 avenue Gaspard Coriolis 31057 Toulouse France;

    CNRM UMR 3589 Universite Federate de Toulouse Meteo-France/CNRS 42 avenue Gaspard Coriolis 31057 Toulouse France;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Urban heat island intensity; Urban morphology; Local Climate Zones; Regression-based models; Random Forest;

    机译：城市热岛强度;城市形态;当地气候区;基于回归的模型;随机森林;

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