Assessing the Vulnerability of Communities Exposed to Climate Change-Related Challenges Caused by the Urban Heat Island Effect Using Machine Learning

Civil infrastructure is a key driver for growth, employment, and better quality of life, which leads to communities transitioning from the natural rural vegetation to urban infrastructure areas. Urbanization exacerbates worrying climate change trends due to man-made activities and increased anthropogenic heat production resulting from urban population growth. This contributes to numerous climate change-related challenges, one of which is the urban heat island (UHI) effect, which affects human health and welfare. While several states in US have experienced high number of heat-related illness cases in the past years, minor research efforts were conducted to determine the areas that are subject to the highest heat-related risks associated with UHI. In relation to that, this paper addresses this knowledge gap by assessing the vulnerability of 95 communities in the state of Tennessee that are exposed to the UHI effect by considering demographic, geographic, climatic, and health factors. To this end, this paper followed an analytical approach based on the integration of unsupervised machine learning algorithms with multiple criteria decision-making methods to cluster or group communities based on 11 UHI-vulnerability-related factors. The results showed that clustering communities based on their vulnerabilities to UHI-related considerations can reveal the most critical geographical areas that are in immediate need to implement strategies that reduce the UHI effect and enhance heat resiliency. Ultimately, this research adds to the body of knowledge by helping states prioritize the design and implementation of optimized urban planning and infrastructure management measures to address UHI and climate change consequences.