Predicting the impacts of land use/land cover changes on seasonal urban thermal characteristics using machine learning algorithms

Changes in land use/land cover (LULC) and land surface temperatures (LST) contribute significantly to the formation and intensity of urban heat islands (UHI) effects. The urban thermal field variance index (UTFVI) can effectively describe any city's UHI (thermal characteristics) effect. This st...

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Published in:	Building and environment Vol. 217; p. 109066
Main Authors:	Kafy, Abdulla - Al, Saha, Milan, Faisal, Abdullah-Al, Rahaman, Zullyadini A., Rahman, Muhammad Tauhidur, Liu, Desheng, Fattah, Md. Abdul, Al Rakib, Abdullah, AlDousari, Ahmad E., Rahaman, Sk Nafiz, Hasan, Md Zakaria, Ahasan, Md Ahasanul Karim
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.06.2022 Elsevier BV
Subjects:	Algorithms Artificial neural networks Cellular automata Gardening Impact prediction Land cover Land cover change Land surface temperature Land use Landsat Learning algorithms Local government Machine learning Machine learning algorithms Neural networks Prediction Remote sensing Sustainable development Thermal comfort Urban areas Urban development Urban heat island Urban heat islands Urban planning Thermal comfort Machine learning algorithms Urban heat island Land cover change Prediction
ISSN:	0360-1323, 1873-684X
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Abstract	Changes in land use/land cover (LULC) and land surface temperatures (LST) contribute significantly to the formation and intensity of urban heat islands (UHI) effects. The urban thermal field variance index (UTFVI) can effectively describe any city's UHI (thermal characteristics) effect. This study aims to assess and predict the seasonal (summer and winter) UTFVI scenario to evaluate the thermal characteristics of Sylhet city, Bangladesh. Landsat 4–5 TM and 8 OLI images from 1995 to 2020 were used to assess the previous status of LULC and UTFVI and predict the future changes for 2025 and 2030 using cellular automata and artificial neural network machine learning algorithms. Prediction results indicate a substantial increase in urban built-up areas by 42% and 44% in 2025 and 2035, followed by reductions in green cover (21% and 22%), bare land (20% and 21%) and water bodies (1%). The rapid expansion of built-up areas will lead to 13 km2 and 14 km2 stronger UTFVI zones in the predicted years. The study provides effective strategies for mitigating the UTFVI effects by avoiding dense infrastructural development, increasing plantation and water bodies, rooftop gardening and using white colour roofs in construction. The findings of this study will allow the urban planners, policymakers and local government to ensure an eco-friendly, inclusive and sustainable urban development through functional modification and replacement of the LULC distribution depending on the present and future circumstances. [Display omitted] •Directional changes of LULC and seasonal UTFVI shift in Sylhet city were analyzed.•Reduction of green cover significantly increase UTFVI effect.•LULC vs UTFVI relationship better explain impacts of different land cover on thermal environment.•Seasonal UTFVI prediction exhibit a gradual decrease in overall thermal characteristics.•Predicted UTFVI vs LULC demonstrated the highest UTFVI concentration in the built-up area.
AbstractList	Changes in land use/land cover (LULC) and land surface temperatures (LST) contribute significantly to the formation and intensity of urban heat islands (UHI) effects. The urban thermal field variance index (UTFVI) can effectively describe any city's UHI (thermal characteristics) effect. This study aims to assess and predict the seasonal (summer and winter) UTFVI scenario to evaluate the thermal characteristics of Sylhet city, Bangladesh. Landsat 4–5 TM and 8 OLI images from 1995 to 2020 were used to assess the previous status of LULC and UTFVI and predict the future changes for 2025 and 2030 using cellular automata and artificial neural network machine learning algorithms. Prediction results indicate a substantial increase in urban built-up areas by 42% and 44% in 2025 and 2035, followed by reductions in green cover (21% and 22%), bare land (20% and 21%) and water bodies (1%). The rapid expansion of built-up areas will lead to 13 km2 and 14 km2 stronger UTFVI zones in the predicted years. The study provides effective strategies for mitigating the UTFVI effects by avoiding dense infrastructural development, increasing plantation and water bodies, rooftop gardening and using white colour roofs in construction. The findings of this study will allow the urban planners, policymakers and local government to ensure an eco-friendly, inclusive and sustainable urban development through functional modification and replacement of the LULC distribution depending on the present and future circumstances. [Display omitted] •Directional changes of LULC and seasonal UTFVI shift in Sylhet city were analyzed.•Reduction of green cover significantly increase UTFVI effect.•LULC vs UTFVI relationship better explain impacts of different land cover on thermal environment.•Seasonal UTFVI prediction exhibit a gradual decrease in overall thermal characteristics.•Predicted UTFVI vs LULC demonstrated the highest UTFVI concentration in the built-up area. Changes in land use/land cover (LULC) and land surface temperatures (LST) contribute significantly to the formation and intensity of urban heat islands (UHI) effects. The urban thermal field variance index (UTFVI) can effectively describe any city's UHI (thermal characteristics) effect. This study aims to assess and predict the seasonal (summer and winter) UTFVI scenario to evaluate the thermal characteristics of Sylhet city, Bangladesh. Landsat 4–5 TM and 8 OLI images from 1995 to 2020 were used to assess the previous status of LULC and UTFVI and predict the future changes for 2025 and 2030 using cellular automata and artificial neural network machine learning algorithms. Prediction results indicate a substantial increase in urban built-up areas by 42% and 44% in 2025 and 2035, followed by reductions in green cover (21% and 22%), bare land (20% and 21%) and water bodies (1%). The rapid expansion of built-up areas will lead to 13 km2 and 14 km2 stronger UTFVI zones in the predicted years. The study provides effective strategies for mitigating the UTFVI effects by avoiding dense infrastructural development, increasing plantation and water bodies, rooftop gardening and using white colour roofs in construction. The findings of this study will allow the urban planners, policymakers and local government to ensure an eco-friendly, inclusive and sustainable urban development through functional modification and replacement of the LULC distribution depending on the present and future circumstances.
ArticleNumber	109066
Author	Rahman, Muhammad Tauhidur AlDousari, Ahmad E. Ahasan, Md Ahasanul Karim Rahaman, Sk Nafiz Kafy, Abdulla - Al Saha, Milan Liu, Desheng Rahaman, Zullyadini A. Fattah, Md. Abdul Faisal, Abdullah-Al Hasan, Md Zakaria Al Rakib, Abdullah
Author_xml	– sequence: 1 givenname: Abdulla - Al surname: Kafy fullname: Kafy, Abdulla - Al email: abdulla-al.kafy@localpathways.org organization: Department of Urban & Regional Planning, Rajshahi University of Engineering & Technology (RUET), Rajshahi, 6204, Bangladesh – sequence: 2 givenname: Milan surname: Saha fullname: Saha, Milan email: milansaha023@gmail.com organization: Department of Urban & Regional Planning, Bangladesh University of Engineering & Technology (BUET), Dhaka, Bangladesh – sequence: 3 givenname: Abdullah-Al surname: Faisal fullname: Faisal, Abdullah-Al email: abdullah-al-faisal@localpathways.org organization: Department of Urban & Regional Planning, Rajshahi University of Engineering & Technology (RUET), Rajshahi, 6204, Bangladesh – sequence: 4 givenname: Zullyadini A. orcidid: 0000-0003-2529-6525 surname: Rahaman fullname: Rahaman, Zullyadini A. email: zully@fsk.upsi.edu.my organization: Department of Geography & Environment, Faculty of Human Sciences, Sultan Idris Education University, Tanjung Malim, 35900, Malaysia – sequence: 5 givenname: Muhammad Tauhidur surname: Rahman fullname: Rahman, Muhammad Tauhidur email: mtr@kfupm.edu.sa organization: Department of City and Regional Planning, King Fahd University of Petroleum & Minerals, KFUPM Box 5053, Dhahran, 31261, Saudi Arabia – sequence: 6 givenname: Desheng surname: Liu fullname: Liu, Desheng email: liu.738@osu.edu organization: Department of Geography, Ohio State University, Columbus, OH, United States – sequence: 7 givenname: Md. Abdul surname: Fattah fullname: Fattah, Md. Abdul email: mafattah.kuet@gmail.com organization: Department of Urban and Regional Planning, Khulna University of Engineering and Technology, Khulna, Bangladesh – sequence: 8 givenname: Abdullah surname: Al Rakib fullname: Al Rakib, Abdullah email: abdullahalrakib310@gmail.com organization: Department of Urban & Regional Planning, Rajshahi University of Engineering & Technology (RUET), Rajshahi, 6204, Bangladesh – sequence: 9 givenname: Ahmad E. surname: AlDousari fullname: AlDousari, Ahmad E. email: dr.dousari@ku.edu.kw organization: Department of Geography, Kuwait University, Kuwait City, Kuwait – sequence: 10 givenname: Sk Nafiz surname: Rahaman fullname: Rahaman, Sk Nafiz email: sknafizrahaman1@gmail.com organization: Urban and Rural Planning Discipline, Khulna University, Khulna, 9208, Bangladesh – sequence: 11 givenname: Md Zakaria surname: Hasan fullname: Hasan, Md Zakaria email: zakariahasan062@gmail.com organization: Department of Geography & Environmental Studies, University of Chittagong, Bangladesh – sequence: 12 givenname: Md Ahasanul Karim surname: Ahasan fullname: Ahasan, Md Ahasanul Karim email: ahasan.karim47@gmail.com organization: Department of Computer Science, Dalhousie University, Canada
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Keywords	Thermal comfort Machine learning algorithms Urban heat island Land cover change Prediction
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Snippet	Changes in land use/land cover (LULC) and land surface temperatures (LST) contribute significantly to the formation and intensity of urban heat islands (UHI)...
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SubjectTerms	Algorithms Artificial neural networks Cellular automata Gardening Impact prediction Land cover Land cover change Land surface temperature Land use Landsat Learning algorithms Local government Machine learning Machine learning algorithms Neural networks Prediction Remote sensing Sustainable development Thermal comfort Urban areas Urban development Urban heat island Urban heat islands Urban planning
Title	Predicting the impacts of land use/land cover changes on seasonal urban thermal characteristics using machine learning algorithms
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