Cooling and optimizing urban heat island based on a thermal knowledge-informed multi-type ant colony model

Abstract

In the context of rapid urbanization and global warming, the urban heat island (UHI) intensifies the risk of heat-related mortality, endangering the health of urban residents. Urban greening effectively mitigates severe urban heating climates, but increasing green space without restrictions is undesirable due to the scarcity of urban land. Accurately characterizing the scope and intensity of UHI and determining the spatial location of the area that needs to be optimized are necessary. In this study, an inverse S-shaped function was used to fit the urban-rural temperature attenuation, whose parameters explicitly describe the properties of UHI. Additionally, a thermal knowledge-informed multi-type ant colony model was proposed to cool UHI automatically. A case study of Wuhan showed that: (1) the fitting effect of the inverse S-shaped function is desirable with the adjusted R2 exceeding 0.97, and the derived parameters with clear physical meanings avoid subjectivity in describing thermal characteristics; (2) high-level heat island of the main urban areas can be reduced by 7.7%–8.5% after land use optimization; and (3) a comparison with the traditional multi-type ant colony model verifies that the model proposed in this study can avoid excessive dispersion of the optimized pixels whose land use types are modified and achieve more reasonable and stable optimization results. This study provides useful exploratory tools for sustainable urban planning, heat mitigation solutions, and other urban retrofitting.