A decision-making framework to support urban heat mitigation by local governments

Abstract

Local governments have made extensive efforts to mitigate urban overheating, cool streetscapes and cities, and protect vulnerable people. However, there is uncertainty about which urban heat mitigation strategies (UHMSs) can provide better solutions for a specific urban context. There is a compelling need for local governments to automate the decision-making process and optimise the combination of UHMSs to maximise the mitigation outcomes for their cities. We develop a novel decision-making framework that incorporates artificial intelligence (AI) techniques into urban heat mitigation in the built environment to enable an automated process of decision making. The novel decision-making framework comprises: the ontology-based knowledge representation of UHMSs and their relationships with urban contexts and performance assessment to share knowledge in urban heat mitigation domain; sensitivity analysis of the environmental, social and economic performance of UHMSs to get key variables for UHMSs; and genetic algorithm-based multi-objective optimisation of UHMSs. The novel decision-making framework enables generating the context-based optimised UHMS combinations to support local governments’ decision-making. The research outcomes will advance interdisciplinary knowledge about using AI techniques in the decision-making process for urban heat mitigation.