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

Abstract

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 4335 km 2 to 8292 km 2 from 2000 to 2030. The LST continues to increase, and the maximum temperature in summer will likely increase to 44.6 °C in 2030. Without the intervention of effective cooling measures, the area with LST≥35 °C will likely increase to 4873 km 2 , and the proportion of areas with LST≥20 °C will likely reach 63.72 % in the winter of 2030. The strongest level of UTFVI expansion is significant in summer, and the area is likely to increase by 83.64 % in 2030. Urban land has the highest percentage in the high temperature region relative to other land use categories. Similar to the trend of LST changes, UHI is expected to notably increase by 2030, with minimum and maximum UHI values projected to rise. This study may provide new perspectives on thermal environment management and sustainable urban development.