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Research Overview on Urban Heat Islands Driven by Computational Intelligence

Author

Listed:
  • Chao Liu

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Siyu Lu

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Jiawei Tian

    (Department of Computer Science and Engineering, Hanyang University, Ansan 15577, Republic of Korea)

  • Lirong Yin

    (Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Lei Wang

    (Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Wenfeng Zheng

    (School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China)

Abstract

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.

Suggested Citation

  • Chao Liu & Siyu Lu & Jiawei Tian & Lirong Yin & Lei Wang & Wenfeng Zheng, 2024. "Research Overview on Urban Heat Islands Driven by Computational Intelligence," Land, MDPI, vol. 13(12), pages 1-26, December.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2176-:d:1543244
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    References listed on IDEAS

    as
    1. Yu Zhang & Yuchen Wang & Nan Ding & Xiaoyan Yang, 2022. "Spatial Pattern Impact of Impervious Surface Density on Urban Heat Island Effect: A Case Study in Xuzhou, China," Land, MDPI, vol. 11(12), pages 1-20, November.
    2. Tingzhen Ming & Shengnan Lian & Yongjia Wu & Tianhao Shi & Chong Peng & Yueping Fang & Renaud de Richter & Nyuk Hien Wong, 2021. "Numerical Investigation on the Urban Heat Island Effect by Using a Porous Media Model," Energies, MDPI, vol. 14(15), pages 1-23, August.
    3. Ze Liang & Yueyao Wang & Jiao Huang & Feili Wei & Shuyao Wu & Jiashu Shen & Fuyue Sun & Shuangcheng Li, 2020. "Seasonal and Diurnal Variations in the Relationships between Urban Form and the Urban Heat Island Effect," Energies, MDPI, vol. 13(22), pages 1-19, November.
    4. Yishao Shi & Jie Wu & Shouzheng Shi, 2017. "Study of the Simulated Expansion Boundary of Construction Land in Shanghai Based on a SLEUTH Model," Sustainability, MDPI, vol. 9(6), pages 1-15, May.
    5. Taleghani, Mohammad, 2018. "Outdoor thermal comfort by different heat mitigation strategies- A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2011-2018.
    6. Ruoning Chen & Xue-yi You, 2020. "Reduction of urban heat island and associated greenhouse gas emissions," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(4), pages 689-711, April.
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