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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Veröffentlicht in:	Building and environment Jg. 217; S. 109066
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 01.06.2022 Elsevier BV
Schlagworte:	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
Online-Zugang:	Volltext
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