Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches.
Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches.
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DOI：
10.1016/j.scitotenv.2021.152836
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
G. Y. Oukawa;P. Krecl;A. Targino
中科院分区：
文献类型：
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作者：
G. Y. Oukawa;P. Krecl;A. Targino
描述城市热岛（UHI）及其驱动因素的时空变化是利用热舒适性创造更健康城市的关键一步，也是增强城市对气候变化的适应力的关键一步。在这项研究中，我们开发了特定的白天和夜间多元线性回归（MLR）和随机森林（RF）模型来分析和预测城市热岛强度（UHII）的时空演变，使用空气温度（Tair）作为响应变量。我们受益于大量的现场Tair数据和一个全面的预测变量池-包括土地覆盖，人口，交通，城市几何，天气数据和大气垂直指数。聚类分析将研究期间分为三个主要组，每个组由天气系统的组合主导，这些天气系统反过来影响UHII的发生和强度。反气旋环流有利于最大UHII的出现（每小时平均5.06 °C），而气旋环流则抑制了它的发展。MLR模型只能解释一个适度的方差百分比（白天和夜间分别为64%和34%），我们将其解释为无法捕获控制Tair的关键因素的一部分。RF模型，另一方面，表现得更好，解释能力超过96%的差异，白天和夜间条件下，捕捉和映射的精细尺度Tairspatiotemporal的变化，在这两个时期和每个集群条件下。特征重要性分析表明，气象变量和土地覆被是主要的预测因子。城市规划者可以从这些结果中受益，使用高性能的RF模型作为预测和减轻城市热岛效应的强大框架。
Characterizing the spatiotemporal variability of the Urban Heat Island (UHI) and its drivers is a key step in leveraging thermal comfort to create not only healthier cities, but also to enhance urban resilience to climate change. In this study, we developed specific daytime and nighttime multiple linear regression (MLR) and random forest (RF) models to analyze and predict the spatiotemporal evolution of the Urban Heat Island intensity (UHII), using the air temperature (Tair) as the response variable. We profited from the wealth ofin situTairdata and a comprehensive pool of predictors variables — including land cover, population, traffic, urban geometry, weather data and atmospheric vertical indices. Cluster analysis divided the study period into three main groups, each dominated by a combination of weather systems that, in turn, influenced the onset and strength of the UHII. Anticyclonic circulations favored the emergence of the largest UHII (hourly mean of 5.06 °C), while cyclonic circulations dampened its development. The MLR models were only able to explain a modest percentage of variance (64 and 34% for daytime and nighttime, respectively), which we interpret as part of their inability to capture key factors controlling Tair. The RF models, on the other hand, performed considerably better, with explanatory power over 96% of the variance for daytime and nighttime conditions, capturing and mapping the fine-scale Tairspatiotemporal variability in both periods and under each cluster condition. The feature importance analysis showed that the meteorological variables and the land cover were the main predictors of the Tair. Urban planners could benefit from these results, using the high-performing RF models as a robust framework for forecasting and mitigating the effects of the UHI.