Machine learning in modelling the urban thermal field variance index and assessing the impacts of urban land expansion on seasonal thermal environment

Land use practices in urban areas exert a profound influence on the urban thermal environment and the pursuit of sustainable development. This paper aims to investigate and forecast future changes in land use/land cover (LULC) and their response to seasonal variations in land surface temperatures (LST), the urban thermal field variance index (UTFVI), and the urban heat island effect (UHI). The artificial neural network based on cellular automata (ANN-CA) and the improved whale optimization based on long short-term memory (WOA-LSTM) algorithms are used to predict the LULC, UTFVI, and UHI characteristics in the Pearl River Delta (PRD) urban agglomeration. The results show that urban land will likely expand from…