标题： 可解释机器学习用于城市热缓解：多尺度驱动因素的归因与加权 显示英文标题

标题： Interpretable Machine Learning for Urban Heat Mitigation: Attribution and Weighting of Multi-Scale Drivers

摘要： 城市热岛（UHIs）在热浪（HWs）期间通常会加剧，并对公共健康构成风险。 缓解城市热岛需要城市规划者首先估计不同土地利用类型（LUTs）和驱动因素如何在不同尺度上影响城市热环境——从大尺度的气候背景过程到小尺度的城市特征和尺度衔接特征。 本研究提出将这些驱动因素分别分类为驱动（D）、城市（U）和局部（L）特征。 为了提高可解释性并增强计算效率，提出了一种区分土地利用类型的机器学习方法，作为天气研究和预报模型耦合单层城市冠层模型（WRF-SLUCM）的快速模拟器，用于预测地表温度（TSK）和2米空气温度（T2）。 使用在2017年和2019年瑞士苏黎世热浪（HW）期间WRF-SLUCM输出上训练的随机森林（RFs）和极端梯度提升（XGB），本研究提出了基于土地利用类型的（LB）模型，该模型根据尺度和实际可控性对特征进行分类，允许选择性地进行类别加权。 这种方法使得可以对T2和TSK对最重要小尺度驱动因素——尤其是表面发射率、反照率和叶面积指数（LAI）进行特定类别的特征排序和敏感性估计。 采用LB框架的模型在统计学上比不采用该框架的模型更准确，当在训练中包含更多热浪数据时表现更好。 由于RF-XGB在单位权重下表现出色，该方法显著提高了可解释性。 尽管需要减少统计不确定性并在其他城市测试该方法，但所提出的方法为城市规划者提供了一个以可行性为中心的城市热岛缓解评估的直接框架。 显示英文摘要

摘要： Urban heat islands (UHIs) are often accentuated during heat waves (HWs) and pose a public health risk. Mitigating UHIs requires urban planners to first estimate how urban heat is influenced by different land use types (LUTs) and drivers across scales - from synoptic-scale climatic background processes to small-scale urban- and scale-bridging features. This study proposes to classify these drivers into driving (D), urban (U), and local (L) features, respectively. To increase interpretability and enhance computation efficiency, a LUT-distinguishing machine learning approach is proposed as a fast emulator for Weather Research and Forecasting model coupled to a Single-Layer Urban Canopy Model (WRF-SLUCM) to predict ground- (TSK) and 2-meter air temperature (T2). Using random forests (RFs) with extreme gradient boosting (XGB) trained on WRF-SLUCM output over Zurich, Switzerland, during heatwave (HW) periods in 2017 and 2019, this study proposes LUT-based (LB) models that categorize features by scales and practical controllability, allowing optional categorical weighting. This approach enables category-specific feature ranking and sensitivity estimation of T2 and TSK to most important small-scale drivers - most notably surface emissivity, albedo, and leaf area index (LAI). Models employing the LB framework are statistically significantly more accurate than models that do not, with higher performance when more HW data is included in training. With RF-XGB robustly performing optimal with unit weights, the method substantially increase interpretability. Despite the needs to reduce statistical uncertainties and testing the method on other cities, the proposed approach offers urban planners a direct framework for feasibility-centered UHI mitigation assessment.