Predicting Urban Heat Island in European Cities: A Comparative Study of GRU, DNN, and ANN Models Using Urban Morphological Variables
Continued urbanization, coupled with anthropogenic global warming, has significantly increased land surface temperatures and air temperature anomalies in urban areas compared to their rural surroundings, leading to Urban Heat Islands (UHI). UHI poses environmental and health risks, impacting both psychological and physiological aspects of human health. This study employs a deep learning approach that incorporates morphological variables to predict UHI intensity in 69 European cities from 2007 to 2021, and projects UHI impacts for 2050 and 2080. The research utilizes Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Gated Recurrent Units (GRU), integrating high-resolution 3D urban models with environmental data to analyze UHI trends. Results reveal strong associations between urban form, weather patterns, and UHI intensity, underscoring the need for tailored urban planning and policy measures to mitigate UHI impacts and promote sustainable urban environments. This research enhances the understanding of UHI dynamics and provides valuable insights for urban planners and policymakers to tackle the challenges of climate change, urbanization, and air pollution, ultimately improving health outcomes and building energy consumption. Additionally, the study demonstrates the effectiveness of the GRU model in linking its projections with UHI impacts, offering crucial insights into potential health effects.
Keywords: Urban Heat Island, deep learning, GRU, DNN, ANN, urban morphology, environmental data, European cities, climate impact projections.
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