Evaluating the Impacts of Natural Based Soluations on Long-term Urban Overheating through Machine Learning and Field Measurements

Urban overheating has become a global issue, exacerbated by climate change and that may lead to severe effects on both public health as well as urban sustainability. This study is intended to permit the prediction of the longevity and severity of future urban overheating events by integrating field measurements and machine learning models, focusing on the impact of urban greening under different global warming (GW) scenarios. Field measurements have been conducted during summer 2024 in an office campus at Ottawa, a city located in cold climate zone. Microclimate data were measured at four locations within the campus, the four locations have different types and coverage levels of urban greenings – large lawn area without trees (Lawn), parking lot without any greening (Parking), greenery area with sparsely distributed trees (Tree) and an area with 100% coverage of trees (Forest).  Models, such as Artificial Neural Networks (ANN), and Recurrent Neural Networks (RNN), and Long Short-Term Memory network models (LSTM) were trained on local microclimate data, with LSTM chosen for its superior performance predictions. Four Global Warming (GW) scenarios were considered to represent different Shared Socioeconomic Pathways (SSP) by 2050 and 2090. The results show that the UTCI at the “Parking” location increased from around 27 °C under GW1.0 to 31 °C under GW3.5. Besides, low health risk (UTCI > 26 °C) will be increased in all locations due to climate change impacts, regardless of urban greening conditions. However, the tree area like 'Tree' and 'Forest' are effective in eliminating the occurrence of extremely high-risk heat conditions (UTCI > 38.9 °C). The findings demonstrate that urban greening plays a crucial role in reducing severe thermal stress, thereby enhancing thermal comfort under future climate scenarios.