Predicting urban Heat Island in European cities: A comparative study of GRU, DNN, and ANN models using urban morphological variables

Abstract

Continued urbanization, along with anthropogenic global warming, has and will increase land surface temperature and air temperature anomalies in urban areas when compared to their rural surroundings, leading to Urban Heat Islands (UHI). UHI poses environmental and health risks, affecting both psychological and physiological aspects of human health. Thus, using a deep learning approach that considers morphological variables, this study predicts UHI intensity in 69 European cities from 2007 to 2021 and projects UHI impacts for 2050 and 2080. The research employs Artificial Neural Networks, Deep Neural Networks, and Gated Recurrent Units, combining high-resolution 3D urban models with environmental data to analyze UHI trends. The results indicate strong associations between urban form, weather patterns, and UHI intensity, highlighting the need for customized urban planning and policy measures to reduce UHI impacts and foster sustainable urban settings. This research enhances understanding of UHI dynamics and serves as a valuable tool for urban planners and policymakers to address the challenges of climate change, urbanization, and air pollution, ultimately aiding in the improvement of health outcomes and building energy consumption. Moreover, the methodology effectively demonstrates the ability of the GRU to link its scores with UHI projections, offering crucial insights into potential health impacts.