ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Interdisciplinary Climate Studies
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1647596
This article is part of the Research TopicHeat Stress and Public Health Issues: Impacts, Adaptation, and MitigationView all 5 articles
Quantifying the Driving Force of Urban Morphologies on Canopy Urban Heat Island: A Machine Learning Approach with Educational Application
Provisionally accepted
- Tongling University, Tongling, China
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This study quantifies the nonlinear driving force of urban morphological factors on canopy urban heat island intensity (CUHII) in Anhui Province, integrating relocated meteorological station data, remote sensing imagery, and machine learning frameworks. CUHII values exhibit a range of 0.06-1.12°C, with the built-up largest patch index (LPIbt, importance score=0.25) and built-up area ratio (ARbt, 0.18) emerging as dominant drivers. Cropland coverage (ARc, Pearson's r=-0.59) demonstrates significant cooling effects on urban thermal environments. The random forest (RF) model outperforms support vector regression (SVR) model, achieving training/test R² values of 0.95/0.76 and RMSE of 0.04/0.08°C. This superiority highlights its capability to capture complex interactions between urban morphologies and local thermal environment. The research framework is innovatively adapted to a flipped classroom educational model: students not only replicate the machine learning workflow using the same dataset but also design comparative experiments to test how urban morphological indicators affect CUHI outputs, thereby deepening their understanding of both physical mechanisms of CUHI and the interpretability of machine learning modeling. This integration of cutting-edge climate research with hands-on educational practice bridges the gap between academic inquiry and practical skill development. The study provides a replicable methodological framework for urban climate research and its translation into educational applications.
Keywords: Urban morphologies, Canopy urban heat island, random forest, Educational application, Methodological framework
Received: 15 Jun 2025; Accepted: 31 Jul 2025.
Copyright: © 2025 Shi, Chen and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tao Shi, Tongling University, Tongling, China
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