Mapping Urban Heatwave Risk with Explainable Spatiotemporal AI: Evidence from Bologna under Climate Change Scenarios

Heatwaves represent one of the most severe climate-related threats to European cities, where their impacts are intensified by urban heat island effects, aging populations, and uneven access to cooling resources and green infrastructure. Although heat-related risks are increasingly acknowledged in urban policy, many existing assessment frameworks continue to rely on conventional formulations that combine hazard, exposure, and vulnerability, grounded in the Intergovernmental Panel on Climate Change (IPCC). Such approaches inadequately capture the complex and dynamic interactions among climate processes, urban morphology, and socio-demographic vulnerability, thereby limiting their usefulness for designing locally targeted and context-specific adaptation strategies.

This study presents a spatiotemporal machine-learning framework for assessing heatwave risk in Bologna, Italy, following the IPCC risk concept. High-resolution environmental, infrastructural, and socio-demographic datasets covering the period 2014–2023 were compiled at the census-tract level. A Long Short-Term Memory (LSTM) neural network was developed to capture temporal dependencies in heatwave risk and optimized using the Hippopotamus Optimization Algorithm to improve predictive performance. The model integrates diverse set of 14 climatological, demographic, economic, and environmental indicators.

Examination of the results indicates a strong spatial agreement between observed and predicted heatwave risk patterns, with classification accuracies exceeding 77% for both low- and high-risk categories. Explainability analysis based on Partial Dependence Plots identifies temperature, vegetation cover, proximity to cooling and healthcare facilities, and the density of elderly female populations as the most influential determinants of heatwave risk. Future projections under RCP 4.5, 6.0, and 8.5 scenarios suggest a substantial expansion of high and very high heatwave risk classes by 2050. This expansion is most pronounced under the RCP 8.5 scenario, where areas classified as very high risk increase from approximately one-third of the urban area to nearly two-thirds.

The findings further highlight the mitigating role of urban green infrastructure, showing that higher vegetation density and improved proximity to green spaces can substantially reduce heatwave risk, albeit with spatially uneven benefits. By combining predictive capability with transparent interpretation, this framework offers practical, fine-scale evidence to support climate adaptation, nature-based solutions, and more equitable heat-resilient urban planning.