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<p>Identifying the key drivers of Surface Urban Heat Islands (SUHIs) is crucial for understanding urban thermal dynamics and informing mitigation strategies. This study models SUHI, examines the contribution of often-neglected atmospheric parameters, identifies key drivers, and pinpoints the spatial regions that most strongly influence SUHI intensity. An explainable deep learning framework integrating multi-source surface and atmospheric data is applied. The framework combines satellite-derived indicators of gray, green, and blue infrastructure, population density, topography from the Digital Elevation Model (DEM), and atmospheric variables from Weather Research and Forecasting (WRF) outputs to predict SUHI intensity. A residual attention-based Convolutional Neural Network (CNN), trained on a 13-year dataset, achieves robust predictive performance, with a Root Mean Square Error (RMSE) of 0.33&#xa0;°C under normal conditions and 0.39&#xa0;°C during heatwaves. Results show that atmospheric parameters, especially wind, are strongly associated with variations in SUHI intensity and contribute to enhanced rural cooling, which is linked to stronger urban–rural temperature contrasts. Precipitation and water vapor influence SUHI by altering evaporative and radiative cooling, while mean sea level pressure has a secondary role. Explainable Artificial Intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM), indicate that urbanization intensity, vegetation, and population density are the primary contributors to SUHI variability, while wind and moisture are associated with changes in thermal contrasts. Spatial analysis further shows that densely built-up urban cores are consistently emphasized in the model’s predictions of SUHI. Grad-CAM highlights spatial interactions between urban and atmospheric parameters. By quantifying surface and atmospheric factors and identifying influential locations, this framework provides accurate SUHI estimates and actionable insights for urban planners, supporting urban ecosystem management and targeted heat adaptation strategies.</p>

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Explainable deep learning for understanding the influence of urban characteristics and atmospheric parameters on surface urban heat islands

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Melika Tasan,
  • Jolanta Dąbrowska

摘要

Identifying the key drivers of Surface Urban Heat Islands (SUHIs) is crucial for understanding urban thermal dynamics and informing mitigation strategies. This study models SUHI, examines the contribution of often-neglected atmospheric parameters, identifies key drivers, and pinpoints the spatial regions that most strongly influence SUHI intensity. An explainable deep learning framework integrating multi-source surface and atmospheric data is applied. The framework combines satellite-derived indicators of gray, green, and blue infrastructure, population density, topography from the Digital Elevation Model (DEM), and atmospheric variables from Weather Research and Forecasting (WRF) outputs to predict SUHI intensity. A residual attention-based Convolutional Neural Network (CNN), trained on a 13-year dataset, achieves robust predictive performance, with a Root Mean Square Error (RMSE) of 0.33 °C under normal conditions and 0.39 °C during heatwaves. Results show that atmospheric parameters, especially wind, are strongly associated with variations in SUHI intensity and contribute to enhanced rural cooling, which is linked to stronger urban–rural temperature contrasts. Precipitation and water vapor influence SUHI by altering evaporative and radiative cooling, while mean sea level pressure has a secondary role. Explainable Artificial Intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM), indicate that urbanization intensity, vegetation, and population density are the primary contributors to SUHI variability, while wind and moisture are associated with changes in thermal contrasts. Spatial analysis further shows that densely built-up urban cores are consistently emphasized in the model’s predictions of SUHI. Grad-CAM highlights spatial interactions between urban and atmospheric parameters. By quantifying surface and atmospheric factors and identifying influential locations, this framework provides accurate SUHI estimates and actionable insights for urban planners, supporting urban ecosystem management and targeted heat adaptation strategies.