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

TL;DR

This paper introduces an interpretable machine learning framework that classifies and weights multi-scale drivers of urban heat, improving prediction accuracy and aiding urban planners in heat mitigation strategies.

Contribution

It proposes a LUT-distinguishing machine learning approach that categorizes drivers by scale and controllability, enhancing interpretability and efficiency in urban heat modeling.

Findings

LUT-based models outperform non-LUT models in accuracy.

Inclusion of heatwave data improves model performance.

Surface emissivity, albedo, and LAI are key drivers for urban heat.

Abstract

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 (WRF) coupled to the Noah land surface model (LSM) to predict ground- (TSK) and 2-meter air temperature (T2). Using random forest regression (RFR) with extreme gradient boosting (XGB) trained on WRF 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 RFR-XGB robustly performing optimal with unit weights, the method substantially increase interpretability. Despite the needs to reduce uncertainties and test the method on other cities, the proposed approach offers urban planners a direct framework for feasibility-centered UHI mitigation assessment.