Artificial intelligence for predicting urban heat island effect and optimising land use/land cover for mitigation: Prospects and recent advancements

Abstract

Rocketing global urbanisation has caused an increase in the Urban Heat Island (UHI) effect, resulting in various negative implications for the urban environment. Quantifying the Surface UHI (SUHI) effect using Land Surface Temperature (LST), Local Climate Zones (LCZ), and deep learning algorithms such as Convolutional Neural Networks (CNN) and pix2pix have prospects in aiding sustainable city planning and modification. Most research on mitigating SUHI promotes greenery as a solution, allowing LCZ optimisation to be explored. Using Heat Vulnerability Index (HVI) and evolutionary algorithms like Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO) show promise in achieving high-quality optimisation solutions. This short communication explores the potential of these artificial intelligence technologies to combat the UHI effect and enhance urban sustainability.