Artículo

Research Overview on Urban Heat Islands Driven by Computational Intelligence

Resumen

In recent years, the intensification of the urban heat island (UHI) effect has become a significant concern as urbanization accelerates. This survey comprehensively explores the current status of surface UHI research, emphasizing the role of land use and land cover changes (LULC) in urban environments. We conducted a systematic review of 8260 journal articles from the Web of Science database, employing bibliometric analysis and keyword co-occurrence analysis using CiteSpace to identify research hotspots and trends. Our investigation reveals that vegetation cover and land use types are the two most critical factors influencing UHI intensity. We analyze various computational intelligence techniques, including machine learning algorithms, cellular automata, and artificial neural networks, used for simulating urban expansion and predicting UHI effects. The study also examines numerical modeling methods, including the Weather Research and Forecasting (WRF) model, while examining the application of Computational Fluid Dynamics (CFD) in urban microclimate research. Furthermore, we evaluate potential mitigation strategies, considering urban planning approaches, green infrastructure solutions, and the use of high-albedo materials. This comprehensive survey not only highlights the critical relationship between land use dynamics and UHIs but also provides a direction for future research in computational intelligence-driven urban climate studies.
Khamisy-Farah, Rola (55808741800); Gilbey, Peter (55980203000); Furstenau, Leonardo B. (57211463471); Sott, Michele Kremer (57218374403); Farah, Raymond (14013894800); Viviani, Maurizio (57231700200); Bisogni, Maurizio (57232852500); Kong, Jude Dzevela (56305065700); Ciliberti, Rosagemma (6507895664); Bragazzi, Nicola Luigi (57212030091)
Big data for biomedical education with a focus on the covid-19 era: An integrative review of the literature
2021
10.3390/ijerph18178989
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113546517&doi=10.3390%2fijerph18178989&partnerID=40&md5=d9dcc718aa0b917324d63a4b1f8d88c9
Clalit Health Service, Akko, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, 13100, Israel; Azrieli Faculty of medicine, Bar Ilan University, Safed, 13100, Israel; Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre, 90035-190, Brazil; Business School, Unisinos University, Porto Alegre, 91330-002, Brazil; Department of Internal Medicine B, Ziv Medical Center, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, 13100, Israel; TransHumanGene, MedicaSwiss, Cham, Zug, 6330, Switzerland; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, ON, Canada; Section of History of Medicine and Bioethics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, 16132, Italy
All Open Access; Gold Open Access; Green Open Access
Scopus
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