Multi-Objective Optimisation Design of Urban Morphology Driven by Climate Adaptation

Urban morphology significantly influences climate processes, and multiple climate elements respond to and provide feedback on urban morphology. In this study, we propose a methodology for monitoring and quantifying the indicators of urban climate elements. Considering the Tiexi District of Shenyang City as the study area, we explore the correlation between urban morphology and various climate elements, screen common indicators of urban morphology parameters as variables and establishe a multiple regression model for urban morphology and climate elements. The results show that the multiple regression model has a good explanation ability for the indicators of climate elements, and the building density, normalised difference vegetation index, impervious surface fraction and sky view factor are the common indicators of the microclimate elements. The road network density and sky view factor are the common indicators of atmospheric environment elements. Further, a mathematical method of multi-objective optimisation is used to integrate the regression functions of various climate-element indicators to obtain a Pareto-optimal urban morphology. Ultimately, the optimised surface temperature, surface humidity, CO2 pollution and PM2.5 pollution magnitudes are determined to be 5.84%, 18.95%, 42.65% and 13.68%, respectively. Thus, we explore a synergistic optimisation method for multiple climate elements considering spatial planning.