Research Overview on Urban Heat Islands Driven by Computational Intelligence
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 environmen...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/13/12/2176 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846104020349878272 |
|---|---|
| author | Chao Liu Siyu Lu Jiawei Tian Lirong Yin Lei Wang Wenfeng Zheng |
| author_facet | Chao Liu Siyu Lu Jiawei Tian Lirong Yin Lei Wang Wenfeng Zheng |
| author_sort | Chao Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9a3a848b3fa7468bbd2d74700e20842e |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-9a3a848b3fa7468bbd2d74700e20842e2024-12-27T14:35:21ZengMDPI AGLand2073-445X2024-12-011312217610.3390/land13122176Research Overview on Urban Heat Islands Driven by Computational IntelligenceChao Liu0Siyu Lu1Jiawei Tian2Lirong Yin3Lei Wang4Wenfeng Zheng5School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaSchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaDepartment of Computer Science and Engineering, Hanyang University, Ansan 15577, Republic of KoreaDepartment of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USADepartment of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USASchool of Automation, University of Electronic Science and Technology of China, Chengdu 610054, ChinaIn 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.https://www.mdpi.com/2073-445X/13/12/2176urban heat island effectland usevegetation indexcomputational intelligenceurban expansion simulationheat island effect prediction |
| spellingShingle | Chao Liu Siyu Lu Jiawei Tian Lirong Yin Lei Wang Wenfeng Zheng Research Overview on Urban Heat Islands Driven by Computational Intelligence Land urban heat island effect land use vegetation index computational intelligence urban expansion simulation heat island effect prediction |
| title | Research Overview on Urban Heat Islands Driven by Computational Intelligence |
| title_full | Research Overview on Urban Heat Islands Driven by Computational Intelligence |
| title_fullStr | Research Overview on Urban Heat Islands Driven by Computational Intelligence |
| title_full_unstemmed | Research Overview on Urban Heat Islands Driven by Computational Intelligence |
| title_short | Research Overview on Urban Heat Islands Driven by Computational Intelligence |
| title_sort | research overview on urban heat islands driven by computational intelligence |
| topic | urban heat island effect land use vegetation index computational intelligence urban expansion simulation heat island effect prediction |
| url | https://www.mdpi.com/2073-445X/13/12/2176 |
| work_keys_str_mv | AT chaoliu researchoverviewonurbanheatislandsdrivenbycomputationalintelligence AT siyulu researchoverviewonurbanheatislandsdrivenbycomputationalintelligence AT jiaweitian researchoverviewonurbanheatislandsdrivenbycomputationalintelligence AT lirongyin researchoverviewonurbanheatislandsdrivenbycomputationalintelligence AT leiwang researchoverviewonurbanheatislandsdrivenbycomputationalintelligence AT wenfengzheng researchoverviewonurbanheatislandsdrivenbycomputationalintelligence |