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Predicting urban Heat Island in European cities: A comparative study of GRU, DNN, and ANN models using urban morphological variables
Journal article   Open access   Peer reviewed

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

Alireza Attarhay Tehrani, Omid Veisi, Kambiz kia, Yasin Delavar, Sasan Bahrami, Saeideh Sobhaninia and Asma Mehan
Urban climate, v 56, 102061
Jul 2024
DOI: https://doi.org/10.1016/j.uclim.2024.102061
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.uclim.2024.102061View
Published, Version of Record (VoR) Open

Abstract

Built environment Deep learning Urban heat island Climate Change Sustainable Development
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. •Analyzes UHI trends across 69 European cities by integrating 3D urban models with environmental data.•Employs Artificial Neural Networks, Deep Neural Networks, and Gated Recurrent Units for accurate UHI prediction.•Shows a high predictive accuracy of UHI with 92% R2 scores using GRU models.•Demonstrates a strong link between urban form, weather patterns, and UHI intensity.

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#11 Sustainable Cities and Communities
#13 Climate Action

Source: SDGs in the Output

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Environmental Sciences
Meteorology & Atmospheric Sciences
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