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Machine Learning Techniques to Map the Impact of Urban Heat Island: Investigating the City of Jeddah

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  • Abdullah Addas

    (Department of Civil Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Alkharj 11942, Saudi Arabia
    Landscape Architecture Department, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

Over the last decades, most agricultural land has been converted into residential colonies to accommodate the rapid population expansion. Population growth and urbanization result in negative consequences on the environment. Such land has experienced various environmental issues due to rapid urbanization and population increases. Such expansion in urbanization has a big impact on worsening the residences soon and in the long term, as the population is projected to increase more and more. One such issue is the urban heat island (UHI), which is computed based on land surface temperature (LST). The UHI effect has fundamental anthropogenic impacts on local areas, particularly in rapidly growing cities. This is due to the unplanned shifts in land use and land cover (LUALC) at the local level, which results in climate condition variations. Therefore, proper planning based on concrete information is the best policy in the long run to remedy these issues. In this study, we attempt to map out UHI phenomena using machine learning (ML) algorithms, including bagging and random subspace. The proposed research also fulfills the sustainable development goals (SDGs) requirement. We exploit the correlation and regression methods to understand the relationship between biophysical composition and the UHI effect. Our findings indicate that in the megacity of Jeddah, Saudi Arabia, from 2000 to 2021, the urban area enlarged by about 80%, while the UHI increased overall. Impervious surfaces significantly impact the UHI effect, while vegetation and water bodies have negative implications for the UHI effect. More than 80% of the total parts in Jeddah have been classified by extremely high UHI conditions, as determined by the bagging and random subspace models. In particular, the megacity’s south, north, and central-east parts were categorized by very high UHI conditions. This research is not only expected to assist in understanding the spatial patterns of the UHI in Jeddah, but to assist planners and policymakers in spatial planning. It will help to ensure sustainable urban management and improve life quality.

Suggested Citation

  • Abdullah Addas, 2023. "Machine Learning Techniques to Map the Impact of Urban Heat Island: Investigating the City of Jeddah," Land, MDPI, vol. 12(6), pages 1-14, May.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:6:p:1159-:d:1160657
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    References listed on IDEAS

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    4. Hongyu Du & Jinquan Ai & Yongli Cai & Hong Jiang & Pudong Liu, 2019. "Combined Effects of the Surface Urban Heat Island with Landscape Composition and Configuration Based on Remote Sensing: A Case Study of Shanghai, China," Sustainability, MDPI, vol. 11(10), pages 1-13, May.
    5. Hua Shi & George Xian & Roger Auch & Kevin Gallo & Qiang Zhou, 2021. "Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology," Land, MDPI, vol. 10(8), pages 1-30, August.
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    Cited by:

    1. Nhat-Duc Hoang & Quoc-Lam Nguyen, 2025. "Geospatial Analysis and Machine Learning Framework for Urban Heat Island Intensity Prediction: Natural Gradient Boosting and Deep Neural Network Regressors with Multisource Remote Sensing Data," Sustainability, MDPI, vol. 17(10), pages 1-26, May.
    2. Jinlong Yan & Chaohui Yin & Zihao An & Bo Mu & Qian Wen & Yingchao Li & Yali Zhang & Weiqiang Chen & Ling Wang & Yang Song, 2023. "The Influence of Urban Form on Land Surface Temperature: A Comprehensive Investigation from 2D Urban Land Use and 3D Buildings," Land, MDPI, vol. 12(9), pages 1-18, September.

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