Nonlinear relations of urban morphology to thermal anomalies: A cross-time comparative study based on Grad-CAM and SHAP

作者：Jingxuan Hu, Tianhui Fan, Xiaolan Tang, Zhijie Yang, Yujie Ren · 发表于：Ecological Indicators · 年份：2024 · DOI：10.1016/j.ecolind.2024.112024 · 被引用次数：32 · 研究领域：Urban Heat Island Mitigation、Land Use and Ecosystem Services、Urban Green Space and Health

Urban thermal anomalies profoundly impact human society, affecting daily life, public health, and residential comfort. Prior studies linked thermal anomalies to urban morphology evolution and land use change during urbanization based on multi-indicator quantification of urban morphology and linear regression modeling. However, it remained unclear which urban morphology elements predominantly dominate thermal anomalies and whether their impact is solely linear, and understanding on the diverse mechanisms through how urban morphology influences various thermal anomalies across seasons remains limited. Therefore, this study employed convolutional neural networks and interpretable machine learning (Grad-CAM and SHAP) to explore nonlinear relationships between urban morphology and thermal anomalies, focusing on comparisons between different types of anomaly events across time. The main findings indicated: (1) Grad-CAM's identification of pivotal hotspot pixels and SHAP's interpretability assessment highlighted that crucial urban morphology factors contributing to thermal anomalies include the area of green spaces, water spaces, the number of residential facilities, building floor area ratio, and the count of industrial production facilities. (2) Clear nonlinear relationships were observed between dominant urban morphology factors and the occurrence of thermal anomalies, which confirming the existence of multiple thresholds and activation levels, as demonstrated through SHAP's part...