Analysing Urban Heat Island Dynamics in Turin: A Machine Learning-based Investigation of Climate Changes and Intervention Effects

This thesis aims to explore the intricate dynamics of urban heat islands (UHIs), a critical concern exacerbated by the combined forces of climate change and urbanization. The primary objective is to develop an urban-scale model, leveraging advanced machine learning algorithms, capable of incorporating a wide array of variables. The study also endeavors to probe the influence of climate change on UHIs through a meticulous examination of historical climate data, seeking to identify any discernible trends that might be linked to the exacerbation of UHIs. Furthermore, the research sets out to assess the efficacy of substantial urban regeneration projects in mitigating the adverse effects of UHIs. A key aspect of this evaluation involves a comparative analysis of data sets collected both prior to and after these interventions, focusing specifically on the years 2001 (pre-intervention) and 2018 (post-intervention). Through these multifaceted approaches, this research seeks to yield valuable insights into UHIs, their interaction with climate change, and the potential of urban revitalization initiatives to mitigate their detrimental impacts, thereby offering valuable guidance for more sustainable urban planning strategies.